Abstract
Pedestrian detection is a hot research topic in pattern recognition and computer vision. We combine MB_LBP (Multiscale Block Local Binary Patterns) feature and Histogram Intersection Kernel SVM and apply them to pedestrian detection. MB_LBP features, which make up for the lack of LBP (Local Binary Patterns) features in robustness, is a kind of effective texture description operator. Histogram Intersection Kernel Support Vector Machine has the advantage of fast classification and high accuracy in object recognition. It can be used for further enhancing the system's real-time performance. The experiments show that the proposed approach has higher precision than the classical algorithm HOG+LinearSVM and the HOG_LBP Features Fusion tested on the established benchmarking datasets—INRIA.
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Acknowldgments
This project is partly supported by NSF of China (61375001), partly supported by the open fund of Key Laboratory of Measurement and partly supported by Control of Complex Systems of Engineering, Ministry of Education (No. MCCSE2013B01), and partly supported by the open project program of Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University (No. CDLS-2014-04), and partly supported by China Postdoctoral Science Foundation (2013M540404),and partly supported by the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120092110024).
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Nian, X., Xie, K., Yang, W., Sun, C. (2015). A Pedestrian Detection Method Based on MB_LBP Features and Intersection Kernel SVM. In: Deng, Z., Li, H. (eds) Proceedings of the 2015 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46469-4_38
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DOI: https://doi.org/10.1007/978-3-662-46469-4_38
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