Abstract: Methods, systems, and apparatuses, among other things, may detect and store activity in videos based on a spatiotemporal graph representation. Spatiotemporal proximity graphs may be built based on one or more received tracks and may include one or more nodes and each node may include one or more attributes associated with a corresponding entity. One or more spatiotemporal relationships may be identified between the entities based on each spatiotemporal proximity graph one or more activities of the entities may be identified based on the spatiotemporal relationships.

Abstract: Methods, systems, and apparatuses, among other things, may detect and store activity in videos based on a spatiotemporal graph representation. Spatiotemporal proximity graphs may be built based on one or more received tracks and may include one or more nodes and each node may include one or more attributes associated with a corresponding entity. One or more spatiotemporal relationships may be identified between the entities based on each spatiotemporal proximity graph one or more activities of the entities may be identified based on the spatiotemporal relationships.

Abstract: Methods, systems, and apparatuses, among other things, may perform persistent object tracking and reidentification through detection and continuous feature comparison. For example, video frames may be received (e.g., from a camera, an application, or a data storage device) and an object of interest may be detected at a first position in a video frame and the object of interest may be detected at a second position in another video frame. A track associated with the object of interest may be generated based on the detected first and second positions of the object of interest.