Structural Recurrent Neural Network (SRNN) for Group Activity Analysis

A group of persons can be analyzed at various semantic levels such as individual actions, their interactions, and the activity of the entire group. In this paper, we propose a structural recurrent neural network (SRNN) that uses a series of interconnected RNNs to jointly capture the actions of indiv...

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Bibliographic Details
Published in2018 IEEE Winter Conference on Applications of Computer Vision (WACV) pp. 1625 - 1632
Main Authors Biswas, Sovan, Gall, Juergen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2018
Subjects
Activity recognition
Computer architecture
Feature extraction
Microprocessors
Recurrent neural networks
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Summary:A group of persons can be analyzed at various semantic levels such as individual actions, their interactions, and the activity of the entire group. In this paper, we propose a structural recurrent neural network (SRNN) that uses a series of interconnected RNNs to jointly capture the actions of individuals, their interactions, as well as the group activity. While previous structural recurrent neural networks assumed that the number of nodes and edges is constant, we use a grid pooling layer to address the fact that the number of individuals in a group can vary. We evaluate two variants of the structural recurrent neural network on the Volleyball Dataset.
DOI:10.1109/WACV.2018.00180