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20 October 2022 Real-time detecting of urban traffic flow based on deep learning
Jia Yan, Lu Lou
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Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124515H (2022) https://doi.org/10.1117/12.2656744
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China
Abstract
Aiming to address the problem of detecting traffic flow in the various complex traffic environments, this paper proposes detection and tracking methods of multi-target moving vehicles based on CenterNet and CenterTrack respectively, and evaluates its performance with public KITTI dataset and four self-collected datasets. The experimental results show that moving vehicles can be effectively detected and tracked in real time under different traffic environments including nighttime, rainy and crowded scenes, the proposed method can reach 97%average detection accuracy with nearly 26fps of tracking speed and is capable of dealing with different traffic and climate conditions. Compared to previous methods, the method proposed in this paper can detect and track the traffic flow more quickly and preciously.
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Jia Yan and Lu Lou "Real-time detecting of urban traffic flow based on deep learning", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124515H (20 October 2022); https://doi.org/10.1117/12.2656744
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KEYWORDS
Detection and tracking algorithms
Target detection
Filtering (signal processing)
Intelligence systems
Video
Visualization
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