Multi-object tracking based on deep aggregation high-resolution network

To improve the performance of multi-object tracking, a multi-object tracking algorithm based on deep aggregation high-resolution network was proposed by combining the characteristics of object detection and re-identification tasks in a single network. The tracker extracted the abstract semantic feature map through deep layer aggregation (DLA) network, and input it into the modified lightweight high resolution network (HRNet) to aggregate the multi-scale features of objects with high resolution. Simultaneously, the re-identification branch was introduced to improve the matching accuracy. The tracking process was completed by traditional similarity calculation, motion prediction and data association stages. The influence of different fusion layer combinations and feature dimensions on tracking performance was studied by ablation experiments, and the performance indexes were compared with those of state-of-the-art trackers on the benchmark datasets. The results show that the proposed algorithm balances the tracking accuracy and execution efficiency with the simple backbone network output of high-resolution deep features. The tracker has high accuracy and recognition rate with realtime tracking performance. © 2023 Southeast University. All rights reserved.