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Learning discriminative context models for concurrent collective activity recognition

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Abstract

Collective activity classification is the task to identify activities with multiple persons participation, which often involves the context information like person relationships and person interactions. Most existing approaches assume that all individuals in a single image share the same activity label. However, in many cases, multiple activities co-exist and serve as context cues for each other in real-world scenarios. Based on this observation, in this paper, a unified discriminative learning framework of multiple context models is proposed for concurrent collective activity recognition. Firstly, both the intra-class and inter-class behaviour interactions among persons in a scenario are considered. Besides, the scenario where activities happen also provides additional context information for recognizing specific collective activities. Finally, we jointly model the multiple context cues (intra-class, inter-class and global-context) with a max-margin leaning framework. A greedy forward search method is utilized to label the activities in the testing scenes. Experimental results demonstrate the superiority of our approach in activity recognition.

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Acknowledgments

This work was supported by 863 Program 2014AA015104, and National Natural Science Foundation of China 61273034, and 61332016.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Chaoyang Zhao, Jinqiao Wang & Hanqing Lu

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  1. Chaoyang Zhao
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  2. Jinqiao Wang
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  3. Hanqing Lu
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Correspondence to Chaoyang Zhao.

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Zhao, C., Wang, J. & Lu, H. Learning discriminative context models for concurrent collective activity recognition. Multimed Tools Appl 76, 7401–7420 (2017). https://doi.org/10.1007/s11042-016-3393-3

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  • DOI: https://doi.org/10.1007/s11042-016-3393-3

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