Human Activity Recognition based on Dynamic Spatio-Temporal Relations

• URL: http://arxiv.org/abs/2006.16132v1
• Date: Mon, 29 Jun 2020 15:49:34 GMT
• Title: Human Activity Recognition based on Dynamic Spatio-Temporal Relations
• Authors: Zhenyu Liu, Yaqiang Yao, Yan Liu, Yuening Zhu, Zhenchao Tao, Lei Wang, Yuhong Feng
• Abstract summary: The description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. We develop a method for human activity recognition that tackles these two issues.
• Score: 10.635134217802783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human activity, which usually consists of several actions, generally covers interactions among persons and or objects. In particular, human actions involve certain spatial and temporal relationships, are the components of more complicated activity, and evolve dynamically over time. Therefore, the description of a single human action and the modeling of the evolution of successive human actions are two major issues in human activity recognition. In this paper, we develop a method for human activity recognition that tackles these two issues. In the proposed method, an activity is divided into several successive actions represented by spatio temporal patterns, and the evolution of these actions are captured by a sequential model. A refined comprehensive spatio temporal graph is utilized to represent a single action, which is a qualitative representation of a human action incorporating both the spatial and temporal relations of the participant objects. Next, a discrete hidden Markov model is applied to model the evolution of action sequences. Moreover, a fully automatic partition method is proposed to divide a long-term human activity video into several human actions based on variational objects and qualitative spatial relations. Finally, a hierarchical decomposition of the human body is introduced to obtain a discriminative representation for a single action. Experimental results on the Cornell Activity Dataset demonstrate the efficiency and effectiveness of the proposed approach, which will enable long videos of human activity to be better recognized.

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