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.
CITATION STYLE
Soudani, A., & Zagrouba, E. (2015). Statistical prior based deformable models for people detection and tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9491, pp. 392–401). Springer Verlag. https://doi.org/10.1007/978-3-319-26555-1_44
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