Crowd Behavior Characterization for Scene Tracking

Abstract : In this work, we perform an in-depth analysis of the specific difficulties a crowded scene dataset raises for tracking algorithms. Starting from the standard characteristics depicting the crowd and their limitations, we introduce six en-tropy measures related to the motion patterns and to the appearance variability of the individuals forming the crowd, and one appearance measure based on Principal Component Analysis. The proposed measures are discussed on synthetic configurations and on multiple real datasets. These criteria are able to characterize the crowd behavior at a more detailed level and may be helpful for evaluating the tracking difficulty of different datasets. The results are in agreement with the perceived difficulty of the scenes.
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Séverine Dubuisson, Isabelle Bloch, Gianni Franchi, Emanuel Aldea. Crowd Behavior Characterization for Scene Tracking. 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Sep 2019, Taipei, Taiwan. pp.1-8, ⟨10.1109/AVSS.2019.8909893⟩. ⟨hal-02472760⟩

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