Histogram-Based Detection of Moving Objects for Tracker Initialization in Surveillance Video
The authors present an approach to localized object detection that is not dependent upon background image construction or object modeling. It is designed to work through camera embedded software using spare processing capacity in a visual signal processor. It uses a localized temporal difference change detector and a particle filter type likelihood to detect possible trackable objects, and to find a point within a detected object at which a particle filter tracker might be initialized. Moving object detection and tracking is central to a range of security and business intelligence surveillance video applications. The information derived from the processes can be used to alert security camera monitoring staff to potential trespass or rule violation in sensitive areas.