Abstract
The traditional pedestrian detection systems usually scan the whole image through sliding window to find the pedestrian, this cause high computation cost. To solve this problem, this paper proposes a regions of interest based fast head detection algorithm. Motivated by the fact that the human head region usually has obvious gradient value and is not easy to be occluded, we set up the initial location model of the region of interest (ROI) by analyzing the distribution of the head gradient. After this, the K-means clustering algorithm is used to filter out the false ROIs and obtain refined candidates. Finally, the HOG-SVM framework is adopted to classify the ROIs after two times of choosing, so as to locate the human heads. Experimental results on real video sequences show that the proposed method can effectively improve the detection rate while ensuring the accuracy of detection.
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Acknowledgements
This work was partially supported by the Natural Science Foundation of China (61203272, 61572224), Natural Science Foundation of Anhui Province (1508085MF116, 1308085MF105), the seventh Batch of ‘115’ Industrial Innovation Team of Anhui Province, Key Project of University Natural Science Research of Anhui Province (KJ2013A237) and the International Science & Technology Cooperation Plan of Anhui Province (10080703003). The Key Program in the Youth Elite Support Plan in Universities of Anhui Province (gxyqZD2016113).
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Li, L., Wang, J. (2016). Fast Head Detection Algorithm via Regions of Interest. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_77
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DOI: https://doi.org/10.1007/978-3-319-46654-5_77
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