Recurrent Modeling of Interaction Context for Collective Activity Recognition

Minsi Wang, Bingbing Ni, Xiaokang Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3048-3056

Abstract


Modeling of high order interactional context, e.g., group interaction, lies in the central of collective/group activity recognition. However, most of the previous activity recognition methods do not offer a flexible and scalable scheme to handle the high order context modeling problem. To explicitly address this fundamental bottleneck, we propose a recurrent interactional context modeling scheme based on LSTM network. By utilizing the information propagation/aggregation capability of LSTM, the proposed scheme unifies the interactional feature modeling process for single person dynamics, intra-group (e.g., persons within a group) and inter-group(e.g., group to group)interactions. The proposed high order context modeling scheme produces more discriminative/descriptive interactional features. It is very flexible to handle a varying number of input instances (e.g., different number of persons in a group or different number of groups) and linearly scalable to high order context modeling problem. Extensive experiments on two benchmark collective/group activity datasets demonstrate the effectiveness of the proposed method.

Related Material


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[bibtex]
@InProceedings{Wang_2017_CVPR,
author = {Wang, Minsi and Ni, Bingbing and Yang, Xiaokang},
title = {Recurrent Modeling of Interaction Context for Collective Activity Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}