基于动态语义聚类关系建模的目标跟踪方法

doi:10.16451/j.cnki.issn1003-6059.202504001

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Abstract

When the Transformer based object tracking method employs the global attention mechanism to model the spatial relations between the search area and the template, the target deformation can lead to the degradation of feature discriminability, causing confusion between the target and the background. To solve this problem, a dynamic semantic clustering relation modeling method for object tracking is proposed. First, a semantic relation modeling module is constructed. Local attention mechanisms in the feature space are employed to concentrate on semantically similar feature vectors, thereby effectively suppressing erroneous interactions between the target and the distracting background. Second, graph neural networks are utilized to capture local correlations and design a dynamic semantic clustering module. The module adaptively generates semantic category partitions, enabling dynamic attention mechanisms to enhance the discriminative information between the target and the background. Finally, a semantic background elimination strategy is designed to effectively suppress the interference from background features during relationship modeling, thereby improving tracking efficiency. Experimental results on six benchmark datasets demonstrate the superiority of the proposed method.

Key words： Object Tracking Self-Attention Feature Learning Graph Neural Network

Received: 04 March 2025

ZTFLH:

TP391

Fund:

Key Research and Development Plan of Heilong- jiang Province(No.GA23A903), Fundamental Research Funds for the Central Universities(No.3072024XX0602), Aeronautical Science Foundation of China(No.202400550P6001)

Corresponding Authors: XU Yuezhu, Ph.D., lecturer. Her research interests include image recognition, multimodal data processing and artificial intelligence.

About author:: NIE Guohao, Ph.D. candidate. His research interests include object tracking and object detection.WANG Xingmei, Ph.D., professor. Her research interests include artificial intelligence, underwater signal processing, compu-ter vision and pattern recognition.Yang Wentao, Master student. His research interests include object tracking and intelligent sensing.

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Cite this article:

NIE Guohao,WANG Xingmei,XU Yuezhu等. Dynamic Semantic Clustering Relation Modeling Method for Object Tracking[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(4): 293-309.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202504001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I4/293

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