Human recognition based on head-shoulder contour extraction and BP neural network

Xiao-fang Kong; Xiu-qin Wang; Guohua Gu; Qian Chen; Wei-xian Qian

doi:10.1117/12.2070804

24 November 2014 Human recognition based on head-shoulder contour extraction and BP neural network

Xiao-fang Kong, Xiu-qin Wang, Guohua Gu, Qian Chen, Wei-xian Qian

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93010Z (2014) https://doi.org/10.1117/12.2070804
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target’s contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

Citation Download Citation

Xiao-fang Kong, Xiu-qin Wang, Guohua Gu, Qian Chen, and Wei-xian Qian "Human recognition based on head-shoulder contour extraction and BP neural network", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93010Z (24 November 2014); https://doi.org/10.1117/12.2070804

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KEYWORDS

Edge detection

Detection and tracking algorithms

Target recognition

Head

Neural networks

Target detection

Binary data

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