Siamese Network Tracking Algorithm Based on Compensated Attention Mechanism
To tackle prevalent challenges in visual object tracking,including variations in target size,motion blur,occlusion,and interference from similar objects,the Compensatory Dual Attention Mechanism(CDAM)-Siam was introduced.This Siamese network tracking algorithm leverages a compensatory attention mechanism for enhanced performance.First,the ResNet-50 network is used to construct the backbone network of the Siamese network for feature extraction at different levels,deepening the network while fully utilizing the features extracted from different layers.The CDAM-Siam algorithm integrates a compensatory dual attention network,enhancing key features and reducing-edge details to improve robustness in complex environments.Finally,a feature fusion network is constructed and added to the backbone network to effectively fuse feature maps from different levels to obtain high-resolution and informative feature maps,ultimately achieving accurate target tracking.After training the CDAM-Siam algorithm on the GOT-10K and YouTube-BB datasets,the detection was performed on the OTB100 dataset.The results showed that the tracking success rate and accuracy of CDAM-Siam were 68.3％and 89.5％,respectively.Despite challenges,the algorithm maintains strong performance,tracking at up to 56 frames per second for real-time requirements.On the VOT2018 dataset,it achieves 53.8％accuracy,39.4％robustness,and a 26.5％Expected Average Overlap(EAO).
万方数据
维普
