Abstract
As an alternative to crowd segmentation using model-based object detection methods which depend on learned appearance models, we propose a paradigm that only makes use of low-level interest points. Here the detection of objects of interest is formulated as a clustering problem. The set of feature points are associated with vertices of a graph. Edges connect vertices based on the plausibility that the two vertices could have been generated from the same object. The task of object detection amounts to identifying a specific set of cliques of this graph. Since the topology of the graph is constrained by a geometric appearance model the maximal cliques can be enumerated directly. Each vertex of the graph can be a member of multiple maximal cliques. We need to find an assignment such that every vertex is only assigned to a single clique. An optimal assignment with respect to a global score function is estimated though a technique akin to soft-assign which can be viewed as a form of relaxation labeling that propagates constraints from regions of low to high ambiguity. No prior knowledge regarding the number of people in the scene is required.
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© 2004 Springer-Verlag Berlin Heidelberg
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Tu, P.H., Rittscher, J. (2004). Crowd Segmentation Through Emergent Labeling. In: Comaniciu, D., Mester, R., Kanatani, K., Suter, D. (eds) Statistical Methods in Video Processing. SMVP 2004. Lecture Notes in Computer Science, vol 3247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30212-4_17
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DOI: https://doi.org/10.1007/978-3-540-30212-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23989-5
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