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