Abstract
In many forensic and data analytics applications there is a need to detect whether and for how long a specific person is present in a video. Frames in which the person cannot be recognized by state of the art engines are of particular importance. We describe a new framework for detection and persistence analysis in noisy and cluttered videos. It combines a new approach to tagging individuals with dynamic person-specific tags, occlusion resolution, and contact re-acquisition. To assure that the tagging is robust to occlusions and partial visibility the tags are built from small pieces of the face surface. To account for the wide and unpredictable ranges of pose and appearance variations and environmental and illumination clutter the tags are continuously and automatically updated by local incremental learning of the object’s background and foreground.
This research was partially supported by: the National Science Foundation, Award # 0916610; two Gifts from the Gerondelis Foundation; the Robert Crooks Stanley Fellowship Fund.
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Kamberov, G., Burlick, M., Karydas, L., Koteoglou, O. (2012). SCAR: Dynamic Adaptation for Person Detection and Persistence Analysis in Unconstrained Videos. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_18
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DOI: https://doi.org/10.1007/978-3-642-33191-6_18
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