Enhancing Multi-Object Tracking with Siamese Network-based Appearance Search

• Jinliang Liu,
• Zheng Lv,
• Jun Zhao,
• Shenglan Liu,

Vol. 18, No. 12, December 31, 2024
10.3837/tiis.2024.12.009, Download Paper (Free):

• computer vision
• Multi-object tracking
• Deep Learning
• Siamese
• Convolutional neural networks

Abstract

Multi-target tracking has made significant progress in recent years as a key player in the field of computer vision, but remains a challenging problem due to target similarity and complexity. In recent years, deep learning has pushed the field forward, and detection-based tracking methods use an end-to-end strategy that unifies target detection and trajectory modeling in a neural network framework, however, little use is made of appearance information. In this paper, we propose a Siamese structure-based approach that introduces an appearance search branch, aiming to enhance the system's ability to model the utilization of target appearance information. The method is validated on the basis of the FairMOT model, which generates a heat map reflecting the results of the target appearance search by means of feature vectors with multiple time dimensions and the Siamese module. The results of the detection branch and the appearance search branch are fused to form a final multi-target tracking system through post-processing and matching. Experiments demonstrate that the method achieves significant performance improvements over existing methods. This research provides a new perspective on the multi-target tracking problem, enhances the modeling and use of target appearance information through the appearance search branch, and provides an effective tool for system performance improvement in complex scenarios.

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