Abstract
This paper presents a new approach to segment and track people in video. The basic idea is the use of deformable model with incorporation of statistical prior. We propose an hybrid energy model that incorporates a global and a statistical based energy terms in order to improve the tracking task even under occlusion conditions. Target models are initialized at the first frame, then predictions are constructed based on motion vectors. Therefore, we apply an hybrid active contour model in order to segment tracked people. Experiments show the ability of the proposed algorithm to detect, segment and track people well.
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Soudani, A., Zagrouba, E. (2015). Statistical Prior Based Deformable Models for People Detection and Tracking. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_44
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DOI: https://doi.org/10.1007/978-3-319-26555-1_44
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