@inproceedings{8164ab1ee2904e42a862666f46112c17,
title = "A pedestrian detection method based on MB\_LBP features and intersection kernel SVM",
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.",
keywords = "HIKSVM, MB\_LBP features, Pedestrian detection",
author = "Xuejie Nian and Ke Xie and Wankou Yang and Changyin Sun",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; Chinese Intelligent Automation Conference, 2015 ; Conference date: 01-01-2015",
year = "2015",
doi = "10.1007/978-3-662-46469-4\_38",
language = "英语",
isbn = "9783662464687",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "361--369",
editor = "Zhidong Deng and Hongbo Li",
booktitle = "Proceedings of the 2015 Chinese Intelligent Automation Conference - Intelligent Information Processing",
}