Please use this identifier to cite or link to this item: https://doi.org/10.1145/2072298.2072025
Title: Human group activity analysis with fusion of motion and appearance information
Authors: Cheng, Z.
Qin, L.
Huang, Q.
Jiang, S.
Yan, S. 
Tian, Q.
Keywords: Activity analysis
Feature fusion
Human group activity
Issue Date: 2011
Source: Cheng, Z.,Qin, L.,Huang, Q.,Jiang, S.,Yan, S.,Tian, Q. (2011). Human group activity analysis with fusion of motion and appearance information. MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops : 1401-1404. ScholarBank@NUS Repository. https://doi.org/10.1145/2072298.2072025
Abstract: Human activity analysis is an important and challenging task in video content analysis and understanding. In this paper, we focus on the activity of small human group, which involves countable persons and complex interactions. To cope with the variant number of participants and inherent interactions within the activity, we propose a hierarchical model with three layers to depict the characteristics at different granularities. In traditional methods, group activity is represented mainly based on motion information, such as human trajectories, but ignoring discriminative appearance information, e.g. the rough sketch of a pose style. In our approach, we take advantage of both the motion and the appearance information in the spatiotemporal activity context under the hierarchical model. These features are inhomogeneous. Therefore, we employ multiple kernel learning methods to fuse the features for group activity recognition. Experiments on a surveillance-like human group activity database demonstrate the validity of our approach and the recognition performance is promising. Copyright 2011 ACM.
Source Title: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
URI: http://scholarbank.nus.edu.sg/handle/10635/70494
ISBN: 9781450306164
DOI: 10.1145/2072298.2072025
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