Density-Based Manifold Collective Clustering for Coherent Motion Detection
Authors: 
Luyang Wang, Guohui Li, Jun Lei, Tao Wang, Yuqian ZhangAuthors Info & Claims
Luyang Wang, Guohui Li, Jun Lei, Tao Wang, Yuqian ZhangAuthors Info & ClaimsPages 22 - 27
Abstract
Detecting coherent motion remains a challenging problem with important applications for the video surveillance and understanding of crowds. In this study, we propose the Density-based Manifold Collective Clustering approach to recognize both local and global coherent motion having arbitrary shapes and varying densities. Firstly, a new manifold distance metric is developed to reveal the underlying patterns with topological manifold structure. Based on the novel definition of collective density, the Density-based collective clustering algorithm is further presented to recognize the local consistency, where its strategy is more adaptive to recognize clusters with arbitrary shapes. Finally, considering the complex interaction among subgroups, a hierarchical collectiveness merging algorithm is introduced to fully characterize the global consistency. Experiments on several challenging video datasets demonstrate the effectiveness of our approach for coherent motion detection, and the comparisons show its superior performance against state-of-the-art competitors.
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Index Terms
- Density-Based Manifold Collective Clustering for Coherent Motion Detection
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Published In
April 2018
81 pages
ISBN:9781450363815
DOI:10.1145/3220511
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- University of Canberra: University of Canberra
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Association for Computing Machinery
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Published: 23 April 2018
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ICMVA 2018
ICMVA 2018: International Conference on Machine Vision and Applications, ICMVA 2018
April 23 - 25, 2018
Singapore, Singapore
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