Research On Algorithm Of Vehicle Tracking Based On Correlation Filter

Intelligent transportation systems are closely related to daily life,not only related to personal safety issues,but also occupy an important position in the development of individuals and countries.Target tracking,as one of the basic problems in the field of computer vision,has great practicality in intelligent transportation systems.Based on the existing tracking algorithms,two tracking algorithms suitable for vehicle targets are designed from the perspectives of scale and features to address the problems of complex and changeable backgrounds and fast motion in road traffic.First,a prediction-based adaptive correlation filter tracking algorithm based on scale is proposed to cope with the problem of rapid scale changes caused by rapid motion in vehicle tracking scenarios.The algorithm adds the scale prediction link to the multi-scale tracking strategy of the existing correlation filter tracking algorithm.First,the forward difference operation is used to obtain the prediction scale,and then the scale pool is built around it and the scale with the largest response value is selected as the target scale.Then use the feedback control principle to dynamically adjust the prediction scale in the next frame,and finally form a closed-loop control to improve the accuracy of the tracking algorithm scale prediction.At the same time,this method adaptively updates the tracking template according to the target moving speed,which can avoid excessive background information learning in the model online learning process.The experimental results on the KITTI and OTB100 data sets show that the algorithm can be well adapted to fast-moving scenes,and its accuracy and success rate are significantly improved compared with the four classic trackers.Second,from the perspective of features,a correlation filtering tracking algorithm combined with deep neural network is designed in order to mine more effective features and improve the discriminatory ability of the tracker in a complex and changing traffic environment.The feature of this algorithm is that it uses a new feature extraction network,which cuts off the feature areas affected by padding to mitigate the effect of padding on translation invariance during the network deepening process,so that the deep features in the convolutional neural network can be effectively applied In the correlation filter tracking algorithm.The experimental results on the KITTI and OTB100 data sets show that the model can effectively utilize the capabilities of deep features,and the method can better cope with vehicle tracking scenarios compared with 5 classic trackers.