Abstract
Video analysis is a rich research topic for a wide spectrum of applications such as surveillance, activity recognition, security, and event detection. Many challenges affect the efficiency of a tracking algorithm such as scene illumination change, occlusions, scaling and search window for the tracked objects. We present an integrated probabilistic model for object tracking, that combines implicit dynamic shape representations and probabilistic object modeling. Furthermore, this paper describes a novel implementation of the algorithm that runs on a general purpose graphics processing unit (GPGPU), and is suitable for video analysis in a real-time vision system. We demonstrate the utility of the proposed tracking algorithm on a benchmark video tracking data set while achieving state-of-the art results in both overlap-accuracy and speed.
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Acknowledgment
The authors acknowledge partial support from NSF grants Nos. 1263011 and 1560345. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
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Smith, K., Smith, A.O. (2016). Video Tracking with Probabilistic Cooccurrence Feature Extraction. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_49
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DOI: https://doi.org/10.1007/978-3-319-50832-0_49
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