Road pedestrian detection and tracking algorithm based on improved YOLOv5s and DeepSORT

Abstract

To address the challenges of low accuracy, high miss detection rate, and poor tracking stability in pedestrian detection and tracking under dense occlusion and small object scenarios on traffic roads, this paper proposes a pedestrian detection and tracking algorithm based on improved YOLOv5s and DeepSORT. For the improvements in the YOLOv5s detection network, first, the Focal-EIoU loss function is used to replace the CIoU loss function. Second, a 160 × 160-pixel Small Object (SO) detection layer is added to the Neck structure. Finally, the Multi-Head Self-Attention (MHSA) mechanism is introduced into the Backbone network to enhance the model’s detection performance. Regarding the improvements in the DeepSORT tracking framework, a lightweight ShuffleNetV2 network is integrated into the appearance feature extraction network, reducing the number of model parameters while maintaining accuracy. Experimental results show that the improved YOLOv5s achieves an mAP0.5 of 80.8% and an mAP0.5:0.95 of 49.7%, representing increases of 4.4% and 3.9%, respectively, compared to the original YOLOv5s. The enhanced YOLOv5s-DeepSORT achieves an MOTA of 50.7% and an MOTP of 77.3%, improving by 3.3% and 0.5%, respectively, over the original YOLOv5s-DeepSORT. Additionally, the number of identity switches (IDs) is reduced by 11.3%, and the model size is reduced to 20% of the original algorithm, enhancing its portability. The proposed method demonstrates strong robustness and can effectively track targets of different sizes.