Abstract
Several video surveillance applications aim at counting the objects present in a scene. Using robust background substraction techniques, detections are unlabelled and often correspond to fragments of objects. Then, a key step for object counting is the association of the fragments representing subparts of a same object. In this work, we model the uncertainty and the imprecision of the location of the detected fragments using Belief Function Theory. Specifically to the case of a video sequence, we propose a data association method between the new detections and the objects already under construction. Tests on actual data were performed. In particular, they allow for the evaluation of the proposed method in term of robustness versus the objects moving.
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Rekik, W., Le Hégarat-Mascle, S., André, C., Kallel, A., Reynaud, R., Ben Hamida, A. (2014). Data Association for Object Enumeration Using Belief Function Theory. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_42
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DOI: https://doi.org/10.1007/978-3-319-11191-9_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11190-2
Online ISBN: 978-3-319-11191-9
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