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Deep Structured Models For Group Activity Recognition

Authors
  • Deng, Zhiwei
  • Zhai, Mengyao
  • Chen, Lei
  • Liu, Yuhao
  • Muralidharan, Srikanth
  • Roshtkhari, Mehrsan Javan
  • Mori, Greg
Type
Preprint
Publication Date
Jun 12, 2015
Submission Date
Jun 12, 2015
Identifiers
arXiv ID: 1506.04191
Source
arXiv
License
Yellow
External links

Abstract

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recognize the actions of individual people in a scene. Next, a neural-network-based hierarchical graphical model refines the predicted labels for each class by considering dependencies between the classes. This refinement step mimics a message-passing step similar to inference in a probabilistic graphical model. We show that this approach can be effective in group activity recognition, with the deep graphical model improving recognition rates over baseline methods.

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