Abstract
Tracking the movements of people within large video surveillance systems is becoming increasingly important in the current security conscious environment. Such system-wide tracking is based on algorithms for tracking a person within a single camera, which typically operate by extracting features that describe the shape, appearance and motion of that person as they are observed in each video frame. These features can be extracted then matched across different cameras to obtain global tracks that span multiple cameras within the surveillance area. In this chapter, we combine a number of such features within a statistical framework to determine the probability of any two tracks being made by the same individual. Techniques are presented to improve the accuracy of the features. These include the application of spatial or temporal smoothing, the identification and removal of significant feature errors, as well as the mitigation of other potential error sources, such as illumination. The results of tracking using individual features and the combined system-wide tracks are presented based upon an analysis of people observed in real surveillance footage. These show that software operating on current camera technology can provide significant assistance to security operators in the system-wide tracking of individual people.
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Madden, C., Piccardi, M. (2011). System-Wide Tracking of Individuals. In: Remagnino, P., Monekosso, D.N., Jain, L.C. (eds) Innovations in Defence Support Systems – 3. Studies in Computational Intelligence, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18278-5_5
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DOI: https://doi.org/10.1007/978-3-642-18278-5_5
Publisher Name: Springer, Berlin, Heidelberg
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