Research On Target Tracking Algorithm Based On Correlation Filter

Target tracking is an indispensable and challenging research topic in the field of computer vision.It plays an important role in security,intelligent transportation,military,human-computer interaction and other fields.Correlation filter tracking uses a circular convolution matrix to convert the space-time domain convolution operation into a product of the transform frequency domain.It has a very efficient calculation speed,however,due to the existence of interference factors such as occlusion,fast motion,blur,illumination and appearance changes.The correlation filter still has problems in that the feature represents a single,the boundary effect produces an erroneous sample,and the target context information is not utilized.In response to these problems,this thesis has carried out in-depth research based on the correlation filtering algorithm.Take the past law as the basis and give it appropriate improvement.Its contents include:Firstly,aiming at the problem of insufficient expression ability of single feature,a correlation filter tracking method based on color feature and direction gradient histogram of decision layer is proposed.The color feature and the direction gradient histogram feature of the target are extracted,and multiple ridge regression classifiers are trained according to different features,and with the help of the latter,several tracking results can be obtained.The weights are adaptively assigned according to the peak sidelobe ratio of the different characteristic response images.Finally,the tracking results are weighted and fused to improve the tracking accuracy.Secondly,aiming at the tracking drift phenomenon in the interference scene,a framework for introducing context patches is proposed,which can be well combined with the correlation filter tracker.The background information of the context patch and the tracking target correlation response value are combined to effectively predict and update the candidate target of the next frame.The tracking effect of the correlation filter tracking algorithm in the interference scenes such as partial occlusion,target deformation and illumination changes is effectively improved.The results of testing the above improvements on the standard tracking test dataset show that compared with the existing correlation filter tracking algorithms,the tracking algorithm in this thesis performs best in tracking accuracy and success rate,and has good robustness.The average center error and pixel error value are smaller than the traditional correlation filtering tracking algorithm.