3D Single-Person Concurrent Activity Detection Using Stacked Relation Network

We aim to detect real-world concurrent activities performed by a single person from a streaming 3D skeleton sequence. Different from most existing works that deal with concurrent activities performed by multiple persons that are seldom correlated, we focus on concurrent activities that are spatiotemporally or causally correlated and performed by a single person. For the sake of generalization, we propose an approach based on a decompositional design to learn a dedicated feature representation for each activity class. To address the scalability issue, we further extend the class-level decompositional design to the postural-primitive level, such that each class-wise representation does not need to be extracted by independent backbones, but through a dedicated weighted aggregation of a shared pool of postural primitives. There are multiple interdependent instances deriving from each decomposition. Thus, we propose Stacked Relation Networks (SRN), with a specialized relation network for each decomposition, so as to enhance the expressiveness of instance-wise representations via the inter-instance relationship modeling. SRN achieves state-of-the-art performance on a public dataset and a newly collected dataset. The relation weights within SRN are interpretable among the activity contexts. The new dataset and code are available at https: //github. com/weiyi1991/UA Concurrent/

Authors

• Yi Wei
• Wenbo Li The Chinese University of Hong Kong
• Yanbo Fan
• Linghan Xu
• Ming-Ching Chang Department of Computer Science, University at Albany - SUNY
• Siwei Lyu

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