Improved KCF Target Tracking Method Based on Kalman Prediction
To address the issues of tracking drift and failure in the kernel correlation filter(KCF)tracking algorithm in occlud-ed environments,an improved KCF tracking method integrating Kalman prediction is proposed.The grayscale image and directional gradient histogram(HOG)feature fusion are used to detect target position.The average peak correlation energy(APCE)is used for target occlusion detection and improving on KCF algorithm.If occlusion occurs,the Kalman filter algorithm is used to predict the tar-get position at the next moment and replace the target in the original KCF algorithm to prevent tracking drift.If no occlusion occurs,the KCF algorithm continues to track,thus achieving effective tracking of targets in occluded environments.The the OTB100 dataset is used for tracking long duration and occlusion sequences in videos.The results show that compared with the KCF algorithm,the proposed algorithm improves the target tracking accuracy and success rate by 11.9%and 13.4%respectively under occlusion,indi-cating a significant improvement in the success rate and tracking accuracy of the proposed algorithm.
万方数据
维普
