A goods sensing system includes: a sample collector that collects a plurality of sets of image samples, where each set of the image samples comprise sample images of a type of goods at multiple angles, where a set of the image samples of a same type of goods are provided with a same group identification, and the group identification is the type of the goods corresponding to the set of image samples; a model trainer that trains a convolutional neural network model according to each sample image and a group identification of the sample image to obtain a goods identification model; a real-time image collector that continuously acquires at least one real-time image of space in front of a shelf, each real-time image including part or all of images of goods; and a goods category deriver that obtains a type and quantity of the goods displayed in the real-time.

A vending machine (100) and a vending method and vending system (1000) therefor, the vending machine (100) comprising a machine body (10), a door body (20), and at least one layer of shelves (30) and a weighing device (40) that are installed inside the machine body (10). The vending method comprises: collecting the weight of products on a shelf before and after vending (S1); calculating the difference of the change between the weight of the products on the shelf before vending and the weight of the products on the shelf after vending (S2); and if the difference of the change is less than a first weight change threshold, issuing an alarm prompt (S3), wherein the first weight change threshold is less than or equal to zero. Using the present invention may promptly determine the misplacement and replacement of the products without relying on an RFID tag.

A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

An item position tracking system includes weight sensors each associated with a weight board. Each weight sensor transmits sensor data indicative of a weight of an item to its corresponding weight board. Each weight board is configured to assign a particular address number to its corresponding weight sensor. The weight boards transmit the sensor data and the address numbers to a circuit board that transmits the sensor data and the address numbers to a weight server. The weight server determines from which weight sensor data is originated based on the address numbers, and whether items were removed from the weight sensors.

An inventory tracking and management system includes storage devices comprising carts, cabinets, or shelves, sensors and/or monitoring devices associated with the storage devices, a central database connecting the storage devices, sensors, and monitoring devices within a hospital, and a processing server associated with the central database. The processing server including a software system controlling operation of the inventory tracking and management system.

A software application that facilitates a method for a user to purchase bulk items from a retailer. The present invention incorporates the use of containers provided by the retailer or the user wherein the containers are assigned a container identifier having as a part thereof the weight of the container. The retailer provides a product identifier for all of the products available for sale. A preferred embodiment of the latter are QR codes or barcodes. The user will identify the product for purchase and scan the product identifier. The product is dispensed into the container after weighing thereof. A plurality of scales are provided to weigh the products wherein the scales are communicably coupled to the smart phone of the user so as to transmit the weight of the products thereto. A shopping list database is created at each shopping excursion that contains all of the products selected for purchase.

An inventory tracking and management system includes storage devices comprising carts, cabinets, or shelves, sensors and/or monitoring devices associated with the storage devices, a central database connecting the storage devices, sensors, and monitoring devices within a hospital, and a processing server associated with the central database. The processing server including a software system controlling operation of the inventory tracking and management system.

A scale-based order management and fulfillment system includes a computer system configured as an order manager, the computer system connected for electronic receipt of customer orders from one or more external devices or systems; and a plurality of scales within a store. The computer system is configured to dynamically process customer orders for fulfillment.

Self-checkout vehicle systems and methods comprising a self-checkout vehicle having a camera(s), a weight sensor(s), and a processor configured to: (i) identify via computer vision a merchandise item selected by a shopper based on an identifier affixed to the selected item, and (ii) calculate a price of the merchandise item based on the identification and weight of the selected item. Computer vision systems and methods for identifying merchandise selected by a shopper comprising a processor configured to: (i) identify an identifier affixed to the selected merchandise and an item category of the selected merchandise, and (ii) compare the identifier and item category identified in each respective image to determine the most likely identification of the merchandise.

An item recognition system uses a top camera and one or more peripheral cameras to identify items. The item recognition system may use image embeddings generated based on images captured by the cameras to generate a concatenated embedding that describes an item depicted in the image. The item recognition system may compare the concatenated embedding to reference embeddings to identify the item. Furthermore, the item recognition system may detect when items are overlapping in an image. For example, the item recognition system may apply an overlap detection model to a top image and a pixel-wise mask for the top image to detect whether an item is overlapping with another in the top image. The item recognition system notifies a user of the overlap if detected.