A multi-objective tracking method based on generative adversarial mechanism is proposed in highway scenes to address the problem of missed detections caused by occlusion in tracking results in multi-target tracking.Firstly,the features output by the pre-trained tracking network are processed,and adaptive two-dimensional occlu-sion masks are added to the feature space to generate occlusion samples that are difficult to obtain in real life.Sec-ondly,to leverage the advantages of generative adversarial networks in unsupervised learning,the FairMOT model is used as the discriminative network,and a generative network combined with reinforcement learning mechanism is added to learn how to filter difficult samples.The two networks were trained adversarially to improve the occlusion invariance of multi-target tracking models and the tracking accuracy was improved.Finally,the center loss function was introduced into the re-identification branch to improve the accuracy of re-identification.Experiments were con-ducted on partial video sequences from the BDD100K dataset.The experimental results show that the improved algo-rithm improves tracking accuracy by 0.8 percentage points,reduces tracking accuracy by 0.4 percentage points,and reduces the number of identity switches during the tracking process by 208 times.
维普
万方数据