Below is an abstract from a paper published at Durham University into behavior tracking via CCTV
This thesis describes work on the automated detection of suspicious pedestrian activity on outdoor CCTV surveillance footage, and in particular, the development of a robust pedestrian tracker. Areas of movement are detected using adaptive background differencing. These detected areas of movement are referred to as silhouettes. Silhouettes having an area larger than a given constant are instantiated as objects. Each object is then classified as a car or a pedestrian by inputting several key features, such as size and aspect ratio, into a multi-layer perceptron neural network. To track effectively, the algorithm must match silhouettes found in the current frame to the objects of the previous frame. It does this by examining the cost of matching a silhouetteobject pair based on simple features such as area, position and a histogram of pixel intensities. The cost is estimated using several self-organising maps to assess how ‘novel’ a matching is compared to a hand-marked reference standard. The algorithm searches through a space of possible object-silhouettes
matchings to find those which yield the lowest global cost. The object positions and features are then updated using the silhouettes to which they are matched. This process is repeated at every frame to produce continuous tracking. The system explicitly deals with merging where silhouettes of two objects merge into a single silhouette, and fragmentation where a single silhouette splits into several silhouettes and must be reconstituted.
Other postings on this subject include: