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19 July 2024 Research on fast pedestrian detection algorithm based on deep learning
Yue Qi
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Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132B (2024) https://doi.org/10.1117/12.3035245
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China
Abstract
Pedestrian detection is one of the most important branches in the field of computer vision. It has great development potential and application prospects. In recent years, the application of intelligent monitoring, unmanned driving and intelligent robots has rapidly promoted the research and development of pedestrian detection. In the dense crowd, the body of a small number of pedestrians cannot be fully displayed, which makes the detection of pedestrians missing. In the reidentification research of specific pedestrian, it is difficult to extract effective pedestrian features due to occlusion, posture and shooting angle. Therefore, in view of the above problems, this paper studies pedestrian detection algorithm to achieve fast and accurate pedestrian positioning, the main research is as follows. In order to improve the accuracy of target recognition of specific pedestrians, based on pedestrian detection, a pedestrian re-identification method based on improved AlignedReID network is proposed. The method combines the DenseNet121 network with less parameters and good generalization performance, and integrates the global and local features of pedestrians to achieve accurate identification of specific pedestrian targets. The experimental results show that on the pedestrian unobstructed Market1501 data set, the Rank- 1 and Rank-5 recognition rates were 93.8% and 97.3%, respectively, and the mAP was 90.5%, which are 1.8%, 0.9% and 2.0% higher than that of the original AlignedReID network.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yue Qi "Research on fast pedestrian detection algorithm based on deep learning", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132B (19 July 2024); https://doi.org/10.1117/12.3035245
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KEYWORDS
Feature extraction
Education and training
Deep learning
Detection and tracking algorithms
Machine learning
Target recognition
Cameras
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