Object detection based on deformable part model

Lei Wei; Zhiyong Xu

doi:10.1117/12.2243219

27 September 2016 Object detection based on deformable part model

Lei Wei, Zhiyong Xu

Proceedings Volume 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment; 96842P (2016) https://doi.org/10.1117/12.2243219
Event: Eighth International Symposium on Advanced Optical Manufacturing and Testing Technology (AOMATT2016), 2016, Suzhou, China

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.

Citation Download Citation

Lei Wei and Zhiyong Xu "Object detection based on deformable part model", Proc. SPIE 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment, 96842P (27 September 2016); https://doi.org/10.1117/12.2243219

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KEYWORDS

Principal component analysis

Image processing

Feature extraction

Data modeling

Detection and tracking algorithms

Statistical modeling

Wavelets

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