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Deep Structured Models For Group Activity Recognition
[article]
2015
arXiv
pre-print
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
arXiv:1506.04191v1
fatcat:43bmyn46zbgthliyctn67h2que