Abstract
In this paper, a two-stage optimal detection algorithm is presented for pedestrians in front of the vehicles. It uses the idea of combing the coarse-grain and fine-grain to effectively classify and filter. First, it uses the combination of Color Self-Similarity features based on rectangular block summing and AdaBoost classifier based on greedy strategy to coarse-grained screen the pedestrian detection window, then it uses the combination of HOG feature and libsvm classifier to fine-grained confirm the previous screened pedestrian detection window, Finally, the target windows is integrated by the greedy strategy. The AdaBoost classifier’s training time is theoretically shorten to the 1/T time of original algorithm. With the training process, The Color Self-Similarity features shorten to 250 dimensions by the feature selection. Then, the method makes full use of the image information and ensures the detection accuracy.
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Acknowledgements
This work was financially supported by the national natural science foundation of China under Grant (No. 61376028) and (No. 61674100).
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Shao, Y., Xu, Mh., Ran, F., Shen, Dy. (2017). A Two-Stage Optimal Detection Algorithm Research for Pedestrians in Front of the Vehicles. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_29
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DOI: https://doi.org/10.1007/978-981-10-6373-2_29
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