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Proceedings Paper

Object detection based on deformable part model
Author(s): Lei Wei; Zhiyong Xu
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Paper Abstract

In complex scene, considering traditional object detection methods based on feature points have exposed many problems, such as undetected points, low detected ratio and cannot well process object occlusion and scaling situation, this paper proposes a detection method which based on a deformable part model. The method uses histogram of oriented gradient (HOG) feature as the object description, and the deformable part model includes a global template and several high-resolution templates. And the method uses the support vector machine (SVM) training the object model. In the learning process, after the HOG feature extracted, the method modifies the HOG feature, and then uses the principal component analysis (PCA) method reducing feature dimensions to avoid over-learning, and improve the detection rate in the detection process. The experiment results shows that the method proposed can better process object occlusion or scaled situation, and there’s also an improvement in detection ratio.

Paper Details

Date Published: 27 September 2016
PDF: 8 pages
Proc. SPIE 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment, 96842P (27 September 2016); doi: 10.1117/12.2243219
Show Author Affiliations
Lei Wei, Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Zhiyong Xu, Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 9684:
8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment
Yudong Zhang; Fan Wu; Ming Xu; Sandy To, Editor(s)

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