Video surveillance system with object detection and probability scoring based on object class
First Claim
1. A method for use in a video surveillance system in which is generated at least a first set of trajectories of a first set of objects of a particular object class hypothesized to have been moving through an area under surveillance at a previous point in time, the method comprisingidentifying objects of said particular class hypothesized to be in said area under surveillance at a current point in time, said particular class of objects being distinguishable from other objects based on the physical appearance of the objects of said particular class, at least one of said objects in said area under surveillance at said current point in time being identified independent of the physical appearance of any objects hypothesized to have been in said area under surveillance at said previous point in time, said identifying including analyzing individual portions of a video image of said area under surveillance to determine if said portions have features that are characteristic of objects in said particular class,extending at least ones of said first set of trajectories to at least ones of said identified objects to develop at least one set of extended trajectories, each of at least ones of said extended trajectories being a respective one of said first set of trajectories extended to at least one of the identified objects, andselecting, as an individual one of said portions, a portion of said video image that is selected independent of whether that portion is in the foreground of said video image.
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Abstract
A video surveillance system uses rule-based reasoning and multiple-hypothesis scoring to detect predefined behaviors based on movement through zone patterns. Trajectory hypothesis spawning allows for trajectory splitting and/or merging and includes local pruning to managed hypothesis growth. Hypotheses are scored based on a number of criteria, illustratively including at least one non-spatial parameter. Connection probabilities computed during the hypothesis spawning process are based on a number of criteria, illustratively including object size. Object detection and probability scoring is illustratively based on object class.
84 Citations
9 Claims
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1. A method for use in a video surveillance system in which is generated at least a first set of trajectories of a first set of objects of a particular object class hypothesized to have been moving through an area under surveillance at a previous point in time, the method comprising
identifying objects of said particular class hypothesized to be in said area under surveillance at a current point in time, said particular class of objects being distinguishable from other objects based on the physical appearance of the objects of said particular class, at least one of said objects in said area under surveillance at said current point in time being identified independent of the physical appearance of any objects hypothesized to have been in said area under surveillance at said previous point in time, said identifying including analyzing individual portions of a video image of said area under surveillance to determine if said portions have features that are characteristic of objects in said particular class, extending at least ones of said first set of trajectories to at least ones of said identified objects to develop at least one set of extended trajectories, each of at least ones of said extended trajectories being a respective one of said first set of trajectories extended to at least one of the identified objects, and selecting, as an individual one of said portions, a portion of said video image that is selected independent of whether that portion is in the foreground of said video image.
Specification