Abstract: A pipeline architecture for analyzing multiple streams of video is embodied, in part, in a layer of application program interfaces (APIs) to each stage of processing. Buffer queuing is used between some stages, which helps moderate the load on the CPU(s). Through the layer of APIs, innumerable video analysis applications can access and analyze video data flowing through the pipeline, and can annotate portions of the video data (e.g., frames and groups of frames), based on the analysis performed, with information that describes the frame or group. These annotated frames and groups flow through the pipeline to subsequent stages of processing, at which increasingly complex analysis can be performed. At each stage, portions of the video data that are of little or no interest are removed from the video data. Ultimately, “events” are constructed and stored in a database, from which cross-event and historical analysis may be performed and associations with, and among, events may be made.

Abstract: A technique is disclosed for determining a group in which to add a new image set. The new image set is captured by one or more cameras in a video surveillance system. Similarity scores are generated between the new image set and one or more image sets of a plurality of groups, wherein each group includes one or more image sets of at least one object. The new image set is added to a group based on one or more factors. Also, a technique is disclosed for determining when to close a group, wherein a closed group is one to which an image set may not be added.

Abstract: A method of identifying an object captured in a video image in a multi-camera video surveillance system is disclosed. Sets of identifying information are stored in profiles, each profile being associated with one object. The disclosed method of identifying an object includes comparing identifying information extracted from images captured by the video surveillance system to one or more stored profiles. A confidence score is calculated for each comparison and used to determine a best match between the extracted set of identifying information and an object. In one embodiment, the method is used as part of a facial recognition system incorporated into a video surveillance system.

Abstract: A method of identifying an object captured in a video image in a multi-camera video surveillance system is disclosed. Sets of identifying information are stored in profiles, each profile being associated with one object. The disclosed method of identifying an object includes comparing identifying information extracted from images captured by the video surveillance system to one or more stored profiles. A confidence score is calculated for each comparison and used to determine a best match between the extracted set of identifying information and an object. In one embodiment, the method is used as part of a facial recognition system incorporated into a video surveillance system.