Cart inspection for suspicious items

Abstract

Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

Claims

1. A system comprising: a transaction area including at least a shopping cart, and a point of sale (POS) terminal that generates transaction data; a video camera positioned proximate to the transaction area, the video camera configured to record POS operator activity and shopping cart images of the transaction area and store recorded video images of a customer in a video repository; and a computer system linked to the POS terminal and the video repository, the computer system comprising at least a processor and a memory, the memory comprising at least an operating system and a cart inspection process, the cart inspection process performing video analysis of images of a transaction near the point of sale terminal, the video analysis comprising comparisons of the images with a reference representation to determine an extent to which a target image portrays imagery of non-suspicious activity or imagery of suspicious activity and, based on the video analysis, generating a suspicion level for the transaction when an image of the transaction portrays one or more items left in the shopping cart at a particular time during the transaction, the cart inspection further comprising: capturing video data from the transaction area proximate to the POS terminal, the video data including images of items from the shopping cart unit; and identifying, based on a determination of cart emptiness, discrepancies between items recorded by the POS terminal and the one or more items detected via analysis of the captured video data based on the items included in the captured video data and omitted by the point of sale terminal; the cart inspection process configured to determine cart emptiness by concluding that the one or more items appropriate for scanning have been scanned by a scanner of the POS terminal.

2. The system of claim 1 wherein identifying discrepancies further comprises identifying at least one item in the captured video data that has no corresponding entry in a transaction record recorded by the point of sale terminal.

3. The system of claim 2, wherein the at least one item in the captured video data that has no corresponding entry in the transaction record captured by the point of sale terminal is an item left in the retail cart unit.

4. The system of claim 1, wherein the determination of cart emptiness is calculated based upon video analysis of the tracked cart.

5. The system of claim 1 wherein the cart inspection process further comprises: detecting cart emptying activity and; calculating at least one point based on a cart's minimum filled point.

6. The system of claim 5 wherein the identified discrepancies are based on the one or more items included in the captured video data and not included in a monetary total recorded by the point of sale terminal.

7. The system of claim 6, further comprising identifying discrepancies occurs based on a predetermined boundary in the transaction area.

8. The system of claim 7, wherein the predetermined boundary is a line proximate to the POS terminal.

9. The system of claim 1 wherein the cart retains not for sale items and unscannable items.

10. The system of claim 9 wherein not for sale items include personal user effects and unscannable items include items having a mass or bulk in excess of a capacity of the scanner.

11. The system of claim 1, wherein the determination of cart emptiness is determined based on cart location within the point of sale area.

12. The system of claim 1, wherein capturing video data includes: capturing video data originating from at least one video camera mounted proximate to the transaction area for capturing images of the cart and images of the scanner.

13. The system of claim 1, further comprising performing video analysis on items detected in the transaction based on a detected presence in the shopping cart and an absence of an item in transaction data resulting from scans by the POS terminal.

14. A system comprising: a transaction area including at least a shopping cart, and a point of sale (POS) terminal that generates transaction data; a video camera positioned proximate to the transaction area, the video camera configured to record POS operator activity and shopping cart images of the transaction area and store recorded video images of a customer in a video repository; and a computer system linked to the POS terminal and the video repository, the computer system comprising at least a processor and a memory, the memory comprising at least an operating system and a cart inspection process, the cart inspection process performing video analysis of images of a transaction near the point of sale terminal, the video analysis comprising comparisons of the images with a reference representation to determine an extent to which a target image portrays imagery of non-suspicious activity or imagery of suspicious activity and, based on the video analysis, generating a suspicion level for the transaction when an image of the transaction portrays one or more items left in the shopping cart at a particular time during the transaction wherein the cart inspection process further comprises: detecting cart emptying activity; detecting cart filling activity and; calculating at least one point as the particular time between emptying activity and filling activity, the filling activity defined by reloading the cart following purchase reconciliation.

15. A system comprising: a transaction area including at least a shopping cart, and a point of sale (POS) terminal that generates transaction data; a video camera positioned proximate to the transaction area, the video camera configured to record POS operator activity and shopping cart images of the transaction area and store recorded video images of a customer in a video repository; and a computer system linked to the POS terminal and the video repository, the computer system comprising at least a processor and a memory, the memory comprising at least an operating system and a cart inspection process, the cart inspection process performing video analysis of images of a transaction near the point of sale terminal, the video analysis comprising comparisons of the images with a reference representation to determine an extent to which a target image portrays imagery of non-suspicious activity or imagery of suspicious activity and, based on the video analysis, generating a suspicion level for the transaction when an image of the transaction portrays one or more items left in the shopping cart at a particular time during the transaction, the cart inspection further comprising: capturing video data from the transaction area proximate to the POS terminal, the video data including images of items from the shopping cart unit; and identifying, based on a determination of cart emptiness, discrepancies between items recorded by the POS terminal and the one or more items detected via analysis of the captured video data based on the items included in the captured video data and omitted by the point of sale terminal, the determination of cart emptiness based on point of sale events, each event defined by scanning of an object from the cart.

16. The system of claim 15, wherein the determination of cart emptiness occurs at the time of the last item recorded by the point of sale terminal.

17. The system of claim 1, wherein the cart inspection process further comprises detecting whether an item in the cart is a bagged, bulk or moving item and whether the item is included in the transaction data.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of embodiments of the methods and apparatus for a Cart Inspector, as illustrated in the accompanying drawings and figures in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, with emphasis instead being placed upon illustrating the embodiments, principles and concepts of the methods and apparatus in accordance with the invention.

(2) FIG. 1 is an example block diagram of Cart Inspector environment according to embodiments herein.

(3) FIG. 2 is an example block diagram of a computer system configured with a Cart Inspector creating a low suspicion level for a transaction according to embodiments herein.

(4) FIG. 3 is an example block diagram of a computer system configured with a Cart Inspector classifying a shopping cart item, which is portrayed in images of a shopping cart, as a non-store item (i.e. a moving item) according to embodiments herein.

(5) FIG. 4 is an example block diagram of a computer system configured with a Cart Inspector classifying a shopping cart item, which is portrayed in an image of a shopping cart, as a bagged item according to embodiments herein.

(6) FIG. 5 is an example block diagram of a computer system configured with a Cart Inspector classifying a shopping cart item as a bulk item according to embodiments herein.

(7) FIG. 6 is an example block diagram of a computer system configured with a Cart Inspector classifying a shopping cart item, which is portrayed in an image of a shopping cart, as a suspicious item according to embodiments herein.

(8) FIG. 7 is an example block diagram illustrating an architecture of a computer system that executes, runs, interprets, operates or otherwise performs a Cart Inspector application and/or Cart Inspector process according to embodiments herein.

(9) FIG. 8 is an example flowchart of processing steps performed by the Cart Inspector to create an indication of a suspicion level for a transaction according to embodiments herein.

(10) FIG. 9 is an example flowchart of processing steps performed by the Cart Inspector to obtain video data associated with a time of interest according to embodiments herein.

(11) FIGS. 10-11 are example flowcharts of processing steps performed by the Cart Inspector to compare an image of a shopping cart with a predefined image of an empty shopping cart according to embodiments herein.

(12) FIG. 12 is an example flowchart of processing steps performed by the Cart Inspector to determine whether a shopping cart item portrayed in an image of a shopping cart qualifies as a moving item, a bagged item, an observable item or a bulk item according to embodiments herein.

(13) FIG. 13 is an example flowchart of processing steps performed by the Cart Inspector to create a minimum suspicion level that represents a least suspicious state of the transaction according to embodiments herein.

DETAILED DESCRIPTION

(14) Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area.

(15) The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction's corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

(16) In one example embodiment, the Cart Inspector obtains a target image of a shopping cart carrying a first item in its main basket and a second item in a compartment beneath the basket. The target image can be a direct overhead view of the shopping cart or an elevated view of the shopping cart. It is understood that the Cart Inspector can perform video analysis on a target image of a shopping cart that contains any number items placed in various regions and compartments of the shopping cart.

(17) Upon detecting a lack of similarity between the target image and a reference representation, the Cart Inspector adjusts the transaction's suspicion level based on the location of the first and second item in the cart, the shape of the first and second item, the color of the first and second item, various other characteristics of the first and second item, and a probability that the first and second item qualify for a false positive classification. Each factor can concurrently decrease or increase the suspicion level according to a predefined amount to result in a final suspicion level of the transaction portrayed in the target image.

(18) FIG. 1 is an example block diagram of Cart Inspector environment 100 according to embodiments herein.

(19) According to the workflow of the transaction area 200, when a shopping cart 210 used during a transaction is near a point of sale terminal 230, it should either be empty or transporting bagged items, bulk items, non-store items (e.g. a child, a handbag, a flier, a pet, etc.), or items that are clearly observable to an operator of the point of sale terminal 230. It is understood that the shopping cart 210 can be any device for transporting goods in the transaction area 200, for example, such as a basket or a dolly.

(20) If the shopping cart 210 contains (or is transporting) bagged items, it is highly likely that the items in the bag were placed on a conveyor belt, scanned by the operator of the point of sale terminal 230, and placed in a shopping bag. The appearance of bagged items in the shopping cart 210 thereby creates a high likelihood that the prices of those items in the shopping bag are included in the total amount to be paid by the customer. Thus, the appearance of a shopping cart 210 containing bagged items when it is in the transaction area 200 is not a suspicious event.

(21) Another non-suspicious event is the appearance of a shopping cart 210 transporting bulk items. Bulk items are those items that are too awkward to ever be placed on the conveyor belt, such as a 36-pack of bottled water. Hence, transactions that include a purchase of a bulk item usually never involve the customer emptying the shopping cart 210 because the bulk item is never place on the downward conveyor belt. Instead, it is customary for the operator to manually enter the price of the bulk item when the shopping cart 210 is near the point of sale terminal 230. Thus, when a shopping cart contains a bulk item, it is also likely that the bulk item's price is included in the total amount to be paid by the customer.

(22) When the shopping cart 210 transports a moving item (i.e. an animated object), such as a child or pet, when it is in the transaction area 200, the appearance of the moving item in the shopping cart 210 is a non-suspicious event as well.

(23) However, if the shopping cart 210 is not empty when it is near the point of sale terminal 250 and the item transported by the shopping cart 210 is not a bagged item, a bulk item, or a moving item, then the item is a loose item. A loose item is an item that most likely was never placed on the conveyor belt and scanned by the operator of the point of sale terminal 250. Hence, the appearance of the loose item in the shopping cart 210 is a suspicious event because the loose item's transportation by the shopping cart 210 indicates that the loose item's price may not be included in the total amount to be paid by the customer.

(24) In order to capture video images of the shopping cart 210 during a time of interest 250 during a transaction (or when the shopping cart 210 is near the point of sale terminal 230 or a scanner 240), the environment 100 includes a video camera 220, placed above the transaction area 200. The video camera 220 records operator and customer activity in the transaction area 200 and stores the recorded video images in a video repository 170.

(25) For example, as illustrated in FIG. 1, at 2 o'clock (i.e. the time of interest 250), the shopping cart 210 is involved in a transaction that involves the purchase of an item 210-1. Since the item 210-1 has been placed on a conveyor belt, the video camera 220 records an image 170-1 of the shopping cart 210 as being empty of any items.

(26) In addition, as the item 210-1 is scanned by the operator of the point of sale terminal 230, transaction data 160 is created. For example, the transaction data can include the time of purchase, the time the item 210-1 was scanned, information identifying the item 210-1, the item price, and total amount paid by the customer.

(27) In order to create an indication of a suspicion level 180 for the purchase of the item 210-1, the Cart Inspector 150 performs video analysis 150-1 of the video image 170-1 that portrays the shopping cart 210 at 2 o'clock.

(28) Since the video image 170-1 (i.e. the target image) shows that the shopping cart 210 was empty at 2 o'clock when it was in the transaction area 200, it is likely that the item's price 210-1 was included in the total amount paid by the customer. The Cart Inspector 150 creates an indication of a low level of suspicion 180 for the transaction recorded in the video image 170-1. Thus, before reviewing the video image 170-1, the indication of the low level of suspicion 180 informs security personnel that the transaction involving the item 210-1 most likely did not result in a financial loss to the retail establishment.

(29) Turning now to FIG. 2, FIG. 2 illustrates an example block diagram of a computer system configured with a Cart Inspector 150 creating a low suspicion level 180 for a transaction according to embodiments herein.

(30) During video analysis 150-1, the Cart Inspector 150-1 obtains a target image. The target image can be a video image 170-1 that shows the shopping cart 210 was empty at 2 o'clock when it was in the transaction area 200. In addition, the Cart Inspector 150-1 obtains a reference representation, which can be a predefined image of an empty cart 150-3.

(31) The Cart Inspector 150 performs an image comparison function 150-2 to compare both images 170-1, 150-3. Since both the images 170-1, 150-3 depict an empty shopping cart, the result 150-2-1 of the image comparison function 150-2 is a detection of a similarity between the images 170-1, 150-3. The similarity between the images 170-1, 150-3 indicates that the transaction recorded in the video image 170-1 that shows the shopping cart 210 was empty at 2 o'clock was most likely a non-suspicious transaction. Based on the video analysis 150-1 of the image 170-1, the Cart Inspector 150 creates an indication of a low suspicion level 180 for the transaction (i.e. the purchase of the item 210-1 and 2 o'clock).

(32) Referring now to FIG. 3, FIG. 3 is an example block diagram of a computer system configured with a Cart Inspector 150 classifying a shopping cart item 210-2, which is portrayed in images 170-2, 170-2-1 of a shopping cart 210, as a moving item according to embodiments herein.

(33) During video analysis 150-1, the Cart Inspector 150-1 obtains a target image, such as a video image 170-2 of a non-empty shopping cart. In addition, the Cart Inspector 150-1 obtains a reference representation such as a predefined image of an empty cart 150-3.

(34) The Cart Inspector 150 performs an image comparison function 150-2 to compare both images 170-2, 150-3. Since the image 170-2 is that of a non-empty shopping cart, the result 150-2-2 of the image comparison function 150-2 is a detection of a difference between the images 170-2, 150-3. The difference between the images 170-2, 150-3 indicates that the transaction recorded in the video image 170-2 may be either an image of a suspicious transaction or an image of a false positive condition.

(35) To determine whether the image 170-2 is an image of a moving item present in a shopping cart (i.e. a false positive condition), the video analysis 150-1 includes a motion detection function 150-4. The motion detection function 150-4 identifies a portion 171 of the non-empty shopping cart image 170-2 which corresponds with the item 210-2. In addition, the Cart Inspector 150 identifies another video image 170-2-1 of the same transaction from the video repository 170. For example, a video image 170-2-1 taken a few seconds later (or earlier) can be obtained by the Cart Inspector 150. The Cart Inspector 150 further identifies a portion 172 of the later image 170-2-1 which corresponds with the item 210-2.

(36) The motion detection function 150-4 processes the two portions 171, 172 in order to identify a motion-based variation between the images' pixels. If the item 210-2 was moving when the images 170-2, 170-2-1 were created, the motion detection function 150-4 results 150-4-1 in a detection of the pixel differences.

(37) Based on the result 150-2-1 of the image compare function 150-2 and the result 150-4-1 of the motion detection function 150-4, an item classifier 150-5 classifies the item 210-2 as a non-store item, such as a moving item (e.g. a child, a pet). Since presence of a moving item in a shopping cart is a non-suspicious event, the Cart Inspector 150 lowers the suspicion level 300 for the transaction with respect to the item's 210-2 presence in the shopping cart.

(38) In another embodiment, the Cart Inspector 150 detects a moving item by applying a time-based recurrent motion measurement. The Cart Inspector 150 uses an item silhouette and a cart silhouette from the non-empty cart image 170-2, and an amount of time to compute a motion value for each pixel in the total item silhouette.

(39) Regarding FIG. 4, FIG. 4 is an example block diagram of a computer system configured with a Cart Inspector classifying a shopping cart item 210-3, which is portrayed in an image 170-3 of a shopping cart, as a bagged item according to embodiments herein. It is understood that the shopping cart item 210-3 can be located anywhere in the shopping cart and need not be in the basket area of the shopping cart for the Cart Inspector 150 to classify the shopping cart item 210-3. For example, the shopping cart item can be situated beneath the basket area of the shopping cart and the Cart Inspector 210-3 will classify the shopping cart item 210-3 as well.

(40) During video analysis 150-1, the Cart Inspector 150-1 obtains a target image, such as, for example, a video image 170-3 of a non-empty shopping cart. In addition, the Cart Inspector 150-1 obtains a reference representation, such as a predefined image of an empty cart 150-3.

(41) The Cart Inspector 150 performs an image comparison function 150-2 to compare both images 170-3, 150-3. Since the image 170-3 is that of a non-empty shopping cart, the result 150-2-3 of the image comparison function 150-2 is a detection of a difference between the images 170-3, 150-3. The difference between the images 170-3, 150-3 indicates that the transaction recorded in the video image 170-3 may be either an image of a suspicious transaction or an image of a false positive condition.

(42) To determine whether the image 170-3 is an image of a bagged item present in a shopping cart (i.e. a false positive condition), the video analysis 150-1 includes a color detection function 150-6. The color detection function 150-6 processes pixels from a portion 173 of the image 170-3 that corresponds with the item 210-3 in the shopping cart.

(43) For example, in one embodiment, the color detection function 150-6 uses a color model on a precompiled set of bag samples. Since store bags are constant and are uniform in color, a probability distribution function (PDF) can be computed for each bag type. A bag confidence level can be generated for each pixel in the portion 173 (e.g. a total item silhouette) by using the PDF to calculate a likelihood that it is a bag pixel.

(44) When the color detection function 150-6 results 150-6-1 in detecting the color of the shopping bag in the portion 173 of the image 170-3 of the non-empty shopping cart, the item classifier 150-5 classifies the item 210-3 as a bagged item. Since presence of a bagged item in a shopping cart is a non-suspicious event, the Cart Inspector 150 lowers the suspicion level 400 for the transaction with respect to the item's 210-3 presence in the shopping cart.

(45) Turning now to FIG. 5, FIG. 5 is an example block diagram of a computer system configured with a Cart Inspector 150 classifying a shopping cart item 210-4 as a bulk item according to embodiments herein.

(46) During video analysis 150-1, the Cart Inspector 150-1 obtains a video image 170-4 of a non-empty shopping cart. In addition, the Cart Inspector 150-1 obtains a predefined image of an empty cart 150-3.

(47) The Cart Inspector 150 performs an image comparison function 150-2 to compare both images 170-4, 150-3. Since the image 170-4 is that of a non-empty shopping cart, the result 150-2-4 of the image comparison function 150-2 is a detection of a difference between the images 170-4, 150-3. The difference between the images 170-4, 150-3 indicates that the transaction recorded in the video image 170-4 may be either an image of a suspicious transaction or an image of a false positive condition.

(48) To determine whether the image 170-4 is an image of a bulk item present in a shopping cart (i.e. a false positive condition), the Cart Inspector 150 performs data analysis 150-7 on transaction data 160. The Cart Inspector 150 obtains a predefined list of bulk items 150-7-1 along with the transaction data 160.

(49) The predefined list of bulk items 150-7-1 describes items that are commonly left in shopping carts during a transaction.

(50) A data compare function 150-8 searches the transaction data 160 for information related to any bulk item listed in the predefined list of bulk items 150-7-1. When the data compare function finds such bulk item information in the transaction data 160, the data compare function 150-8 creates a result 150-8-1 indicating the presence of such bulk item information.

(51) Based on the result, 150-8-1, the item classifier 150-5 classifies the item 210-4 as a bulk item. Since presence of a bulk item in a shopping cart is a non-suspicious event, the Cart Inspector 150 lowers the suspicion level 500 for the transaction with respect to the item's 210-4 presence in the shopping cart.

(52) In another embodiment, for large, heavy, or awkward items, it is common for the operator of a point of sale terminal 230 to scan the item 210-4 with a hand scanner or enter the item's identification number into the point of sale terminal 230 by hand. To detect bulk items, the Cart Inspector 150 obtains a precompiled item library (including image templates, features, item visual representations, etc.). This library defines those large, heavy, or awkward items that are customarily left in shopping carts by customers during a transaction.

(53) The Cart Inspector 150 defines a segment of the image 170-4 of the non-empty cart. The segment includes the representation of the item 210-4 portrayed in the image 170-4 of the non-empty cart.

(54) When a transaction completes, all of the item information from the transaction data 160 is obtained. The precompiled item library is then queried for visual representations (e.g. images, templates, geometric property information) of each item described in the transaction data 160.

(55) The Cart Inspector 250 compares the visual representation of each item described in the transaction data with the segment that includes the representation of the item 210-4 portrayed in the image 170-4 of the non-empty cart. If the segment correlates with any of the visual representations of the items described in the transaction data 160, then the suspicion level for the transaction is decreased with respect the item 210-4. However, if the segment fails to correlate with any of the visual representations of the items described in the transaction data 160, then the suspicion level for the transaction is increased with respect the item 210-4.

(56) There are many methods available for image comparison including histogram color analysis, geometric analysis, and edge comparison analysis. One embodiment employs the use of a multi-resolution correlation technique. The images in the database are transformed into a pyramid image using a wavelet transform. A correlation score is computed and a match is determined by comparing against a confidence threshold. Those items that have no matches are considered suspicious.

(57) FIG. 6 is an example block diagram of a computer system configured with a Cart Inspector 150 classifying a shopping cart item, which is portrayed in an image 170-6 of a shopping cart, as a suspicious item according to embodiments herein.

(58) The Cart Inspector 150 performs an image comparison function 150-2 to compare both images 170-6, 150-3. Since the image 170-6 is that of a non-empty shopping cart, the result 150-2-5 of the image comparison function 150-2 is a detection of a difference between the images 170-6, 150-3. The difference between the images 170-6, 150-3 indicates that the transaction recorded in the video image 170-6 may be either an image of a suspicious transaction or an image of a false positive condition. Thus, if the Cart Inspector 150 detects that no false positive condition exists, then the image 170-6 of the non-empty cart most likely is a recording of a suspicious transaction.

(59) To determine whether the image 170-6 is an image of a moving item present in a shopping cart (i.e. a false positive condition), the video analysis 150-1 performs the motion detection function 150-4. If the item was moving when it was in the shopping cart, the motion detection function 150-4 results in a detection of the pixel differences (as discussed above with regard to FIG. 3). However; based on video analysis 150-1 involving the image 170-6 of the non-empty shopping cart, the results 150-4-2 of the motion detection function 150-4 fails to detect pixel difference. Thus, the item in the shopping cart portrayed in the image 170-6 is most likely not a moving item.

(60) To determine whether the image 170-6 is an image of a bagged item present in a shopping cart (i.e. a false positive condition), the video analysis 150-1 performs the color detection function 150-6. The color detection function 150-6 processes pixels from a portion of the image 170-6 that corresponds with the item in the shopping cart. Based on video analysis 150-1, the results 150-6-2 of the color detection function 150-6 fails to detect a distribution of color corresponding with a shopping bag. Thus, the item in the shopping cart portrayed in the image 170-6 is most likely not a bagged item.

(61) To determine whether the image 170-6 is an image of a bulk item present in a shopping cart (i.e. a false positive condition), the Cart Inspector 150 performs data analysis 150-7 on transaction data 160. The data compare function 150-8 searches the transaction data 160 for information related to any bulk item listed in the predefined list of bulk items 150-7-1. When the data compare function 150-8 fails to find bulk item information in the transaction data 160, the data compare function 150-8 creates a result 150-8-2 indicating that there is no bulk item information in the transaction data 160.

(62) Since the item portrayed in the image 170-6 as present in the shopping cart is not a moving item, a bulk item, or a bagged item, it is highly likely that the item was never placed on the conveyor belt and/or scanned by the operator of the point of sale terminal 230. Thus, there is a probability that the item's price was not included in the total price paid by the customer. The item classifier 150-5 thereby classifies the item as a suspicious item 150-5-5 which increases the suspicion level 700 for the transaction with respect to the item portrayed in the image 170-6 as present in the shopping cart.

(63) FIG. 7 is an example block diagram illustrating an architecture of a computer system 110 that executes, runs, interprets, operates or otherwise performs a Cart Inspector application 150-10 and/or Cart Inspector process 150-11 (e.g. an executing version of a Cart Inspector 150 as controlled or configured by user 108) according to embodiments herein.

(64) Note that the computer system 110 may be any type of computerized device such as a personal computer, a client computer system, workstation, portable computing device, console, laptop, network terminal, etc. This list is not exhaustive and is provided as an example of different possible embodiments.

(65) In addition to a single computer embodiment, computer system 110 can include any number of computer systems in a network environment to carry the embodiments as described herein.

(66) As shown in the present example, the computer system 110 includes an interconnection mechanism 111 such as a data bus, motherboard or other circuitry that couples a memory system 112, a processor 113, an input/output interface 114, and a display 130. If so configured, the display can be used to present a graphical user interface of the Cart Inspector 150 to user 108. An input device 116 (e.g., one or more user/developer controlled devices such as a keyboard, mouse, touch pad, etc.) couples to the computer system 110 and processor 113 through an input/output (I/O) interface 114. The computer system 110 can be a client system and/or a server system. As mentioned above, depending on the embodiment, the Cart Inspector application 150-10 and/or the Cart Inspector process 150-11 can be distributed and executed in multiple nodes in a computer network environment or performed locally on a single computer.

(67) During operation of the computer system 110, the processor 113 accesses the memory system 112 via the interconnect 111 in order to launch, run, execute, interpret or otherwise perform the logic instructions of the Cart Inspector application 150-1. Execution of the Cart Inspector application 150-10 in this manner produces the Cart Inspector process 150-2. In other words, the Cart Inspector process 150-11 represents one or more portions or runtime instances of the Cart Inspector application 150-10 (or the entire application 150-1) performing or executing within or upon the processor 113 in the computerized device 110 at runtime.

(68) The Cart Inspector application 150-10 may be stored on a computer readable medium (such as a floppy disk), hard disk, electronic, magnetic, optical, or other computer readable medium. It is understood that embodiments and techniques discussed herein are well suited for other applications as well.

(69) Those skilled in the art will understand that the computer system 110 may include other processes and/or software and hardware components, such as an operating system. Display 130 need not be coupled directly to computer system 110. For example, the Cart Inspector application 150-10 can be executed on a remotely accessible computerized device via the communication interface 115.

(70) Regarding the flowcharts 900, 1000, 1100, 1200 1300 and 1400, FIG. 8 through FIG. 13 illustrate various embodiment of the Cart Inspector 150. The rectangular elements in flowcharts 900, 1000, 1100, 1200, 1300 and 1400 represent processing blocks and represent computer software instructions or groups of instructions upon a computer readable medium. Additionally, the processing blocks represent steps performed by hardware such as a computer, digital signal processor circuit, application specific integrated circuit (ASIC), etc.

(71) Flowcharts 900, 1000, 1100, 1200, 1300 and 1400 do not necessarily depict the syntax of any particular programming language. Rather, flowcharts 900, 1000, 1100, 1200, 1300 and 1400 illustrate the functional information one of ordinary skill in the art requires to fabricate circuits or to generate computer software to perform the processing required in accordance with the present invention. It will be appreciated by those of ordinary skill in the art that unless otherwise indicated herein, the particular sequence of steps described is illustrative only and may be varied without departing from the spirit of the invention. Thus, unless otherwise stated, the steps described below are unordered, meaning that, when possible, the steps may be performed in any convenient or desirable order.

(72) FIG. 8 is an example flowchart 900 of processing steps performed by the Cart Inspector 150 to create an indication of a suspicion level 180 for a transaction according to embodiments herein. At step 910, the Cart Inspector 150 obtains video data associated with a time of interest 250. The video data originates from a video camera 220 that monitors a transaction area 200.

(73) At step 920, the Cart Inspector 150 analyzes the video data 170-1 with respect to an image of a cart 170-1 involved in a transaction in the transaction area 200 during the time of interest 250. The video data includes the image of the cart 170-1.

(74) At step 930, the Cart Inspector 150 creates an indication of a suspicion level 180 for the transaction based on analysis of the one image of the cart 170-1 provided in the video data.

(75) FIG. 9 is an example flowchart 1000 of processing steps performed by the Cart Inspector to obtain video data associated with a time of interest 250 according to embodiments herein.

(76) At step 1010, the Cart Inspector 150 identifies a time stamp in transaction data 160 of a transaction.

(77) At step 1020, the Cart Inspector 150 identifies the time stamp as the last time stamp that appears in the transaction data 160.

(78) At step 1030, the Cart Inspector 150 defines the one time of interest 250 as contemporaneous with the time stamp.

(79) At step 1040, the Cart Inspector 150 identifies a portion(s) of the video data created by the video camera 220 during the time of interest 250 which contain an image(s) of the cart 170-1 in the transaction area during time of interest 250.

(80) In another embodiment, the Cart Inspector 150 defines a critical location in the transaction area 200, such as the location of a scanning device 240 or the point of sale terminal 230.

(81) The Cart Inspector 150 defines the time of interest 250 as when the cart is present at (or proximate to) the critical location (e.g. the scanning device 240, point of sale terminal 230) in the transaction area 200.

(82) The Cart Inspector 150 identifies a portion of the video data, created by the video camera 220 during the time of interest 250, which contains an image 170-1 of the cart at the critical location in the transaction area 200 during the time of interest 250.

(83) FIGS. 10-11 are example flowcharts 1100, 1200 of processing steps performed by the Cart Inspector 150 to compare an image of a shopping cart 170-1 with a predefined image of an empty shopping cart 150-3 according to embodiments herein.

(84) At step 1110, the Cart Inspector 150 performs a comparison of the image of the cart 170-1 with a predefined image of an empty cart 150-3 to determine whether the image of the cart 170-1 portrays an empty cart.

(85) At step 1120, if the comparison results in detection of a similarity between the two images 170-1, 150-3, the Cart Inspector 150 sets the indication of the suspicion level to a lowest suspicion level 180.

(86) As illustrated in FIG. 11, at step 1130, if the comparison results in detection of a difference between the two images 170-1, 150-3, the Cart Inspector 150 performs steps 1140-1160 for each portion of the image of the cart 170-1 that portrays an item(s) in the cart:

(87) At step 1140, the Cart Inspector 150 determines whether the item (e.g. item 210-2, 210-3, 210-4 or 210-5) in the cart qualifies for a classification.

(88) At step 1150, in response to the item (e.g. item 210-2, 210-3, 210-4 or 210-5) in the cart qualifying for a classification, the Cart Inspector 150 decreases the suspicion level 300, 400, 500 for the transaction.

(89) At step 1160, in response to the item (e.g. item 210-2, 210-3, 210-4 or 210-5) in the cart failing to qualify for any classification, the Cart Inspector 150 increases the suspicion level for the transaction.

(90) FIG. 12 is an example flowchart 1300 of processing steps performed by the Cart Inspector 150 to determine whether a shopping cart item (e.g. item 210-2, 210-3, 210-4 or 210-5) portrayed in an image of a shopping cart qualifies as a moving item, a bagged item, an observable item or a bulk item according to embodiments herein.

(91) To process an occurrence of a first false positive condition, at step 1310, the Cart Inspector 150 detects an indication of movement over time in a portions 171, 172 of images of the cart 170-2, 170-2-1 that portray an item 210-2 in the cart.

(92) At step 1320, upon detecting the indication of movement, the Cart Inspector 150 classifies the item 210-2 in the cart as a non-store item 150-5-1, such a moving item (e.g. a child, a pet).

(93) To process an occurrence of a second false positive condition, at step 1330, the Cart Inspector 150 detects a distribution of a color in a portion 173 of an image of the cart 170-3 that portrays the item 210-3 in the cart. The detected color corresponds to a shopping bag used in the transaction area 200.

(94) At step 1340, upon detecting the distribution of the color, the Cart Inspector 150 classifies the item 210-3 in the cart as a bagged item 150-5-2.

(95) To process an occurrence of a third false positive condition, at step 1350, the Cart Inspector 150 receives a signal that detected a placement of an item 210-5 in a region within the cart during the time of interest 250. The placement of the item 210-5 signifies a likelihood that the item 210-5 is not involved in a suspicious transaction.

(96) At step 1360, upon receipt of the signal, the Cart Inspector 150 classifies the item as an observable item 150-5-4.

(97) To process an occurrence of a fourth false positive condition, at step 1370, the Cart Inspector 150 classifies the item 210-4 in the cart as a bulk item 150-5-3 upon detecting information in the transaction data 160 that is related to a predefined bulk item.

(98) In another embodiment, the Cart Inspector 150 obtains video data associated with a time of interest 250. The video data originates a video camera(s) 220 that monitors a transaction area 200. For example, the video camera(s) 220 can be elevated over a horizontal plane where the transaction occurs in the transaction area 220, such that the video camera(s) 220 record transactions in the transaction area 220 for a vantage point above the transaction area 200

(99) FIG. 13 is an example flowchart 1400 of processing steps performed by the Cart Inspector 150 to create a minimum suspicion level that represents a least suspicious state of the transaction according to embodiments herein.

(100) At step 1410, the Cart Inspector 150 obtains video data from a video camera(s) 220 that monitors a transaction area 200.

(101) At step 1420, the Cart Inspector 150 analyzes a plurality of target images in the video data associated with a transaction in the transaction area 200. Thus, the Cart Inspector 150 analyzes each video frame created by the camera 200 during the transaction.

(102) At step 1430, based on analysis of each of the plurality of target images, the Cart Inspector 150 identifies a portion of the plurality of target images that represent a least suspicious state of the transaction.

(103) At step 1440, the Cart Inspector 150 identifies at least one least-suspicious image that represents a time of interest during the transaction that a cart 210 is least likely to contain unpurchased merchandise items.

(104) At step 1440, the Cart Inspector 150 creates a minimum suspicion level that represents the least suspicious state of the transaction.

(105) For example, the Cart Inspector 150 performs the video analysis as discussed throughout this document upon each video frame created for the transaction. By doing so, a suspicion level is created for each video frame of the transaction. Hence, one of the video frames will have a lowest suspicion level as compared to the other video frames of the transaction. The Cart Inspector 150 identifies the video frame with the lowest suspicion level as a least-suspicious image of that transaction because the lowest suspicion level assigned to that video frame represents a point in time (or a location in the transaction area) where the transaction was at its least suspicious state.

(106) Note again that techniques herein are well suited for a Cart Inspector 150 that performs video analysis 150-1 of target images 170-1, 170-2, 170-3, 170-5, 170-6 that portray a transaction near a point of sale terminal 230. Based on the video analysis 150-1, a suspicion level 300, 400, 500, 600, 700 for the transaction is created when the target image 170-1, 170-2, 170-3, 170-5, 170-6 portrays an item(s) 210-2, 210-3, 210-4, 210-5, 210-6 transported through a transaction area 200 at a particular time 250 during the transaction.

(107) The methods and systems described herein are not limited to a particular hardware or software configuration, and may find applicability in many computing or processing environments. The methods and systems may be implemented in hardware or software, or a combination of hardware and software. The methods and systems may be implemented in one or more computer programs, where a computer program may be understood to include one or more processor executable instructions. The computer program(s) may execute on one or more programmable processors, and may be stored on one or more storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), one or more input devices, and/or one or more output devices. The processor thus may access one or more input devices to obtain input data, and may access one or more output devices to communicate output data. The input and/or output devices may include one or more of the following: Random Access Memory (RAM), Redundant Array of Independent Disks (RAID), floppy drive, CD, DVD, magnetic disk, internal hard drive, external hard drive, memory stick, or other storage device capable of being accessed by a processor as provided herein, where such aforementioned examples are not exhaustive, and are for illustration and not limitation.

(108) The computer program(s) may be implemented using one or more high level procedural or object-oriented programming languages to communicate with a computer system; however, the program(s) may be implemented in assembly or machine language, if desired. The language may be compiled or interpreted.

(109) As provided herein, the processor(s) may thus be embedded in one or more devices that may be operated independently or together in a networked environment, where the network may include, for example, a Local Area Network (LAN), wide area network (WAN), and/or may include an intranet and/or the Internet and/or another network. The network(s) may be wired or wireless or a combination thereof and may use one or more communications protocols to facilitate communications between the different processors. The processors may be configured for distributed processing and may utilize, in some embodiments, a client-server model as needed. Accordingly, the methods and systems may utilize multiple processors and/or processor devices, and the processor instructions may be divided amongst such single- or multiple-processor/devices.

(110) The device(s) or computer systems that integrate with the processor(s) may include, for example, a personal computer(s), workstation(s) (e.g., Sun, HP), personal digital assistant(s) (PDA(s)), handheld device(s) such as cellular telephone(s), laptop(s), handheld computer(s), or another device(s) capable of being integrated with a processor(s) that may operate as provided herein. Accordingly, the devices provided herein are not exhaustive and are provided for illustration and not limitation.

(111) References to a processor, or the processor, may be understood to include one or more microprocessors that may communicate in a stand-alone and/or a distributed environment(s), and may thus be configured to communicate via wired or wireless communications with other processors, where such one or more processor may be configured to operate on one or more processor-controlled devices that may be similar or different devices. Use of such processor terminology may thus also be understood to include a central processing unit, an arithmetic logic unit, an application-specific integrated circuit (IC), and/or a task engine, with such examples provided for illustration and not limitation.

(112) Furthermore, references to memory, unless otherwise specified, may include one or more processor-readable and accessible memory elements and/or components that may be internal to the processor-controlled device, external to the processor-controlled device, and/or may be accessed via a wired or wireless network using a variety of communications protocols, and unless otherwise specified, may be arranged to include a combination of external and internal memory devices, where such memory may be contiguous and/or partitioned based on the application.

(113) Throughout the entirety of the present disclosure, use of the articles a or an to modify a noun may be understood to be used for convenience and to include one, or more than one of the modified noun, unless otherwise specifically stated.

(114) Elements, components, modules, and/or parts thereof that are described and/or otherwise portrayed through the figures to communicate with, be associated with, and/or be based on, something else, may be understood to so communicate, be associated with, and or be based on in a direct and/or indirect manner, unless otherwise stipulated herein.

(115) Although the methods and systems have been described relative to a specific embodiment thereof, they are not so limited. Obviously many modifications and variations may become apparent in light of the above teachings. Many additional changes in the details, materials, and arrangement of parts, herein described and illustrated, may be made by those skilled in the art.