A method for indexing objects in a computerized system having an index, comprising identifying in the computerized system an at least one indexed object that meets an at least one criterion related to contents of the at least one indexed object, detecting an at least one non-indexed object having a property similar to an at least one property of the at least one indexed object that was identified, and indexing the at least one non-indexed object in the index, wherein the method is performed by the computerized system, and an apparatus for performing the same.

Systems and methods are described herein for detecting that a person of interest is displayed in a live video for a threshold amount of time, attempting to identify an identity of the person determining that the attempting has failed, identifying broadcast times corresponding to a period of time in which the person was displayed in the live video, identifying trending topics in a social media space that relate to the live video, ascertaining an identity of the person of interest based on the trending topics, comparing the identity of the person of interest to a database of social media profiles, identifying a social media component that corresponds to the person, determining whether the person has modified the social media component within a predefined period of time, and providing information relating to a modification of the social media component simultaneously with the video.

A mechanism is described for facilitating recognition, reidentification, and security in machine learning at autonomous machines. A method of embodiments, as described herein, includes facilitating a camera to detect one or more objects within a physical vicinity, the one or more objects including a person, and the physical vicinity including a house, where detecting includes capturing one or more images of one or more portions of a body of the person. The method may further include extracting body features based on the one or more portions of the body, comparing the extracted body features with feature vectors stored at a database, and building a classification model based on the extracted body features over a period of time to facilitate recognition or reidentification of the person independent of facial recognition of the person.

Systems and methods are described herein for detecting that a person of interest is displayed in a live video for a threshold amount of time, attempting to identify an identity of the person determining that the attempting has failed, identifying broadcast times corresponding to a period of time in which the person was displayed in the live video, identifying trending topics in a social media space that relate to the live video, ascertaining an identity of the person of interest based on the trending topics, comparing the identity of the person of interest to a database of social media profiles, identifying a social media component that corresponds to the person, determining whether the person has modified the social media component within a predefined period of time, and providing information relating to a modification of the social media component simultaneously with the video.

A method, apparatus and system for acquiring headcount are provided. Data frames are acquired from a surveillance video, object models are set in the data frames, and a foreground image is extracted from the data frames, wherein the foreground image includes moving objects extracted from the data frames; the moving objects in the foreground image is matched with the object models, the headcount in corresponding data frames is acquired according to a result of the matching, characteristics of the matched moving objects are extracted, and the corresponding moving objects are tracked based on the characteristics; the actual headcount for a predetermined time segment of the surveillance video is acquired based on a result of the tracking and the acquired headcount for each of the data frames, wherein the result of the tracking includes: an indication of whether some or all of the data frames includes a same moving object.

A method and apparatus for characteristic-based video processing include: in response to receiving a region of a picture of a video sequence, determining a characteristic in the region, the region being independent of other regions of the picture for video coding; determining a class associated with the region based on the characteristic, the class being selected from a plurality of classes; and encoding the region using a parameter set associated with the class, the parameter set being selected from a plurality of parameter sets for video coding at different quality levels.

Systems and methods for increasing an efficiency of an operator of a security system are discussed generally herein. A system can include a memory including ontology data saved thereon, the ontology data can define interrelationships between a scanner associated with access to a room of an area under surveillance, a camera with a field of view at least partially overlapping a footprint of the room, an identifier configured to be scanned by the scanner and associated with a person, and a security policy including one or more predefined conditions, which when satisfied, indicate when a security threat exists, the security policy includes a response an operator can perform if the conditions are satisfied, and the system can include a query module configured to receive a query and search the ontology data and temporal and spatial data associated with the area under surveillance in response to receiving the query.

A first retriever performs, based on a person feature of a person extracted from a video image before an acceptance time point of a retrieval instruction of a target person and stored and a feature extracted from the target person related to the retrieval instruction, a first retrieving process of retrieving the target person from the video image stored, a second retriever performs, based on a feature of a person extracted from a video image after the acceptance time point and the feature of the target person extracted from a query image of the retrieval instruction, a second retrieving process of retrieving the target person from the video image input after the acceptance time point, and the first retriever performs the first retrieving process to the video image input during a period from the acceptance time point to preparation completion of the second retrieving process and second retrieving process start.

The invention provides a method for processing and analyzing forensic video data using a computer program, the method comprising the steps of recording the forensic video data; providing supplementary data related to the recorded video data, wherein the supplementary data may be provided from or input by a source external of the computer program, in particular a human, or wherein the supplementary data may be extracted from the forensic video data by the computer program in an initial analyzing step; analyzing the forensic video data by the computer program using the supplementary data; and displaying a part of the forensic video data, the displayed part being based on a result of the analyzing step.

The various implementations described herein include systems and methods for recognizing persons in video streams. In one aspect, a method includes: obtaining images collected by video cameras in a smart home environment, each image including a detected person; for each image, obtaining first information that identifies an attribute of the detected person, the first information generated from analysis of the image; grouping the images into a first group of a plurality of groups based on the first information, each group of the plurality of groups representing a unique person; receiving a request to remove a first image from the first group; removing the first image from the first group; and disassociating the first information from the first group.