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9 January 2025 An improved YOLOv5s model based on DeepSORT for pedestrian detection and tracking
Kai Shu, Huacai Lu, Jie Xu, Yunchang Shen
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Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134862A (2025) https://doi.org/10.1117/12.3055982
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China
Abstract
Various problems afflict the complex network structures that are implemented in pedestrian detection and tracking applications, including a large number of parameters, lengthy training times, and slow running speeds. In this study, we proposed an improved pedestrian detection and tracking model based on YOLOv5s detection and DeepSORT tracking and used the video Mosaic algorithm based on the scale-invariant feature transform (SIFT) algorithm to expand the scope of pedestrian detection and tracking by addressing the aforementioned problems. We implemented MnasNet_P, cavity convolution, and SPP layers in our model. The Mish activation function replaced the LeakyReLU activation function to improve the generalizability of the model. A depth-separable convolution was introduced to replace the standard convolution of residual edges in the C3_1 structure, reducing the number of network parameters. Shufflenetv2_x1.5 was introduced as a pedestrian appearance feature extraction network, further reducing the number of network parameters while maintaining high tracking accuracy. Application of our model to public datasets demonstrated an improvement of 2.71% in average accuracy and an improvement of 30.7% in the FPS rate. Experimental results obtained with the MOT16 dataset demonstrated a substantially reduced model size while maintaining a high tracking accuracy, indicating that our algorithm is suitable for pedestrian tracking on mobile terminals or embedded devices. The real-time video stitching method based on SIFT expanded the tracking range of pedestrians.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Kai Shu, Huacai Lu, Jie Xu, and Yunchang Shen "An improved YOLOv5s model based on DeepSORT for pedestrian detection and tracking", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134862A (9 January 2025); https://doi.org/10.1117/12.3055982
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KEYWORDS
Detection and tracking algorithms
Convolution
Evolutionary algorithms
Feature extraction
Ablation
Data modeling
Target detection
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