Abstract
Multi-channel feature for pedestrian detection is proposed to solve problems of real-time and accuracy of pedestrian detection in this paper. Different from traditional low level feature extraction algorithm, channels such as colours, gradient magnitude and gradient histogram are combined to extract multi-channel feature for describing pedestrian. Then classifier is trained by AdaBoost algorithm. Finally the performance of the algorithm is tested in MATLAB. The result demonstrates that the algorithm has an excellent performance on both detection precision and speed.
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Acknowledgement
This work was financially supported by the national natural science foundation of China under Grant (No. 61376028) and (No. 61674100).
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He, Z., Xu, M., Guo, A. (2017). Multi-channel Feature for Pedestrian Detection. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_47
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DOI: https://doi.org/10.1007/978-981-10-6370-1_47
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