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Recognition of Human Group Activity for Video Analytics

Jaeyong Ju, Cheoljong Yang, Sebastian Scherer and Hanseok Ko
Conference Paper, Carnegie Mellon University, Advances in Multimedia Information Processing, pp. 735, September, 2015

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Abstract

Human activity recognition is an important and challenging task for video content analysis and understanding. Individual activity recognition has been well studied recently. However, recognizing the activities of human group with more than three people having complex interactions is still a formidable challenge. In this paper, a novel human group activity recognition method is proposed to deal with complex situation where there are multiple sub-groups. To characterize the inherent interactions of intra-subgroups and inter-subgroups with the varying number of participants, this paper proposes three types of group-activity descriptor using motion trajectory and appearance information of people. Experimental results on a public human group activity dataset demonstrate effectiveness of the proposed method.

BibTeX Reference
@conference{Scherer-2015-101812,
title = {Recognition of Human Group Activity for Video Analytics},
author = {Jaeyong Ju and Cheoljong Yang and Sebastian Scherer and Hanseok Ko},
booktitle = {Advances in Multimedia Information Processing},
publisher = {Springer, Cham},
school = {Robotics Institute , Carnegie Mellon University},
month = {September},
year = {2015},
pages = {735},
address = {Pittsburgh, PA},
}
2017-11-03T10:15:15+00:00