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has been cited by the following article:

• TITLE: Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition

  AUTHORS: Hao Zheng, Jianfang Liu, Mengyi Liao

  KEYWORDS: YOLOv3, Local Optical Flow Method, Human Behavior Recognition

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.1, January 8, 2021

  ABSTRACT: In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.