Mr. Xuesen Li Profile

Xuesen Li

at North China Electric Power Univ

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Proceedings Article | 20 May 2011 Paper

3D abnormal behavior recognition in power generation

Zhenhua Wei, Xuesen Li, Jie Su, Jie Lin

Proceedings Volume 8043, 80430Y (2011) https://doi.org/10.1117/12.883819

KEYWORDS: 3D modeling, Detection and tracking algorithms, Cameras, Mobile devices, Bone, Edge detection, Mobile communications, Control systems, Statistical modeling, Bridges

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So far most research of human behavior recognition focus on simple individual behavior, such as wave, crouch, jump and bend. This paper will focus on abnormal behavior with objects carrying in power generation. Such as using mobile communication device in main control room, taking helmet off during working and lying down in high place. Taking account of the color and shape are fixed, we adopted edge detecting by color tracking to recognize object in worker. This paper introduces a method, which using geometric character of skeleton and its angle to express sequence of three-dimensional human behavior data. Then adopting Semi-join critical step Hidden Markov Model, weighing probability of critical steps' output to reduce the computational complexity. Training model for every behavior, mean while select some skeleton frames from 3D behavior sample to form a critical step set. This set is a bridge linking 2D observation behavior with 3D human joints feature. The 3D reconstruction is not required during the 2D behavior recognition phase. In the beginning of recognition progress, finding the best match for every frame of 2D observed sample in 3D skeleton set. After that, 2D observed skeleton frames sample will be identified as a specifically 3D behavior by behavior-classifier. The effectiveness of the proposed algorithm is demonstrated with experiments in similar power generation environment.

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