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Learning Sparse Prototypes for Crowd Perception via Ensemble Coding Mechanisms

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 8749)

Abstract

Recent work in cognitive psychology suggests that crowd perception may be based on pre-attentive ensemble coding mechanisms consistent with feedforward hierarchical models of visual processing. Here, we extend a biological model of motion processing with a new dictionary learning method tailored for crowd perception. Our approach uses a sparse coding model to learn crowd prototypes. Ensemble coding mechanisms are implemented via structural and local coherence constraints. We evaluate the proposed method on multiple crowd perception problems from collective or abnormal crowd detection to tracking individuals in crowded scenes. Experimental results on crowd datasets demonstrate competitive results on par or better than state-of-the-art approaches.

Keywords

  • Sparse coding
  • Crowd perception
  • Biological vision

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Zhang, Y., Zhang, S., Huang, Q., Serre, T. (2014). Learning Sparse Prototypes for Crowd Perception via Ensemble Coding Mechanisms. In: Park, H.S., Salah, A.A., Lee, Y.J., Morency, LP., Sheikh, Y., Cucchiara, R. (eds) Human Behavior Understanding. HBU 2014. Lecture Notes in Computer Science, vol 8749. Springer, Cham. https://doi.org/10.1007/978-3-319-11839-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-11839-0_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11838-3

  • Online ISBN: 978-3-319-11839-0

  • eBook Packages: Computer ScienceComputer Science (R0)