Pedestrian multiobject tracking is the major research branch in the field of computer vision. In complicated scenarios with frequent scale changes and occlusion, the existing multiobject tracking methods based on detection have unsatisfactory tracking accuracy because of the low robustness of reidentification. This article proposed a multiobject tracking method to improve the reidentification module in YOLOv5-DeepSORT at a more fine-grained level. The feature extraction network for the Re-ID part of this algorithm is built based on Res2Net and group convolution. This network's hierarchical connection structure effectively improved the network's ability to extract multiscale features, and at the same time increased the receptive field of each network layer. The PCB network structure with evenly divided feature maps is used in the output part of the backbone network to enhance the influence of local features on the overall network performance. Based on this, the reidentification model is trained on the public datasets Market-1501 and DukeMTMC-reID using triplet loss. ER-DeepSORT is an algorithm that combined the improved reidentification module of this article into DeepSORT. This article compared ER-DeepSORT with YOLOv5-DeepSORT under the original reidentification module to evaluate the tracking effect in MOT16 test sequence, the experimental results showed that ER-DeepSORT improved MOTA by 5.4% and MOTP by 2.2% on the Market-1501 datasets, and improved MOTA by 9.6% and MOTP by 2.7% on the DukeMTMC-reID datasets.

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