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arXiv · 1506.04191

Deep Structured Models For Group Activity Recognition

Zhiwei Deng·Mengyao Zhai·Lei Chen·Yuhao Liu·Srikanth Muralidharan·Mehrsan Javan Roshtkhari·Greg Mori
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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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Zhiwei Deng, Mengyao Zhai, Lei Chen, Yuhao Liu, Srikanth Muralidharan, Mehrsan Javan Roshtkhari, Greg Mori. 2015-06-12. Deep Structured Models For Group Activity Recognition. https://arxiv.org/abs/1506.04191

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