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Segmentation of crowd flow by trajectory clustering in active contours

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Abstract

Crowd analysis has become an important topic of research for visual surveillance community. This paper proposes an active contour-based trajectory clustering approach for crowd flow segmentation. To this end, the active contour method is applied to segment the foreground crowd region with an aim to optimize further tracking. From the segmented foreground region, spatiotemporal interest points are detected and tracked to extract crowd trajectories. The trajectories are then parameterized by their shape, location information, flow direction, and neighborhood density. A clustering algorithm is designed to cluster these trajectories, and further flow patterns are segmented by merging trajectory clusters on the basis of their spatial overlapping and distinction in location and in flow direction. Once the flow patterns are segmented, trajectory density of each segment is estimated to analyze crowd flow. Experiments are conducted on three publicly available UCF Web, Collective Motion, and Violent Flows crowd datasets. The proposed work is compared with various state-of-the-art methods and achieves remarkable accuracy while maintaining the lower computational complexity.

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  1. Malaviya National Institute of Technology, Jaipur, India

    Sonu Lamba & Neeta Nain

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  1. Sonu Lamba
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  2. Neeta Nain
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Correspondence to Sonu Lamba.

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Sonu Lamba declares that she has no conflict of interest. Neeta Nain declares that she has no conflict of interest.

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Lamba, S., Nain, N. Segmentation of crowd flow by trajectory clustering in active contours. Vis Comput 36, 989–1000 (2020). https://doi.org/10.1007/s00371-019-01713-7

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