Abstract
Pedestrian detection is an essential technology in robotics, intelligent transportation system, and intelligent video surveillance. Pedestrians in the surveillance scene have the characteristics of dense crowds and high degree of occlusion, meanwhile, it needs to meet the requirements of real-time detection. To solve this problem, the method based on head model with YOLOv3 algorithm was proposed. This work re-selects the number and dimensions of anchor boxes by k-means method on the training set, fine-tuning the network and training it to get the optimal model. The experiment results show that this work effectively improves the detection performance and real-time of pedestrian detection in the surveillance scene.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Nos. 61472282, 61672035, and 61872004), Anhui Province Funds for Excellent Youth Scholars in Colleges (gxyqZD2016068), the fund of Co-Innovation Center for Information Supply & Assurance Technology in AHU (ADXXBZ201705), and Anhui Scientific Research Foundation for Returned Scholars.
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Lu, P., Lu, K., Wang, W., Zhang, J., Chen, P., Wang, B. (2019). Real-Time Pedestrian Detection in Monitoring Scene Based on Head Model. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_53
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DOI: https://doi.org/10.1007/978-3-030-26969-2_53
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