A Vehicle Tracking Method Based on Attention Mechanism in Complex Scenarios
Visual-based vehicle tracking techniques often encounter tracking drift issues due to complex backgrounds,low-resolution images,and variations in lighting conditions.To address these challenges and enhance tracking performance in complex scenarios,a novel visual vehicle tracking algorithm based on attention mechanisms is proposed.The proposed algorithm leverages the attention-based Swin Transformer to effectively explore and represent features,enabling comprehensive modeling of global information.Furthermore,an attention-based encoder is employed to fuse and augment the gathered information,harnessing the full potential of attention mechanisms.Finally,a simple yet stacked RepVGG network is employed to accurately predict the position of the tracked vehicle.Experimental evaluations conducted on two public large-scale benchmark datasets,LaSOT and UAV123.It achieves precision of 78.4%and 89.6%,as well as success rates of 69.3%and 69.8%on the respective datasets.Moreover,the proposed algorithm outperforms existing tracking methods,showcasing its effectiveness.Additionally,extensive visualizations and analysis are conducted on vehicle video sequences from the OTB100 dataset.The results are better than the benchmark STARK-S50.It has more stable tracking performance and can resist various tracking challenges such as complex backgrounds,blurring,similar objects,occlusion,dim lighting,vehicle scale transformation and rotation.