Improved Correlation Filter Tracking with Enhanced Features and Adaptive Kalman Filter.

ABSTRACT: In the field of visual tracking, discriminative correlation filter (DCF)-based trackers have made remarkable achievements with their high computational efficiency. The crucial challenge that still remains is how to construct qualified samples without boundary effects and redetect occluded targets. In this paper a feature-enhanced discriminative correlation filter (FEDCF) tracker is proposed, which utilizes the color statistical model to strengthen the texture features (like the histograms of oriented gradient of HOG) and uses the spatial-prior function to suppress the boundary effects. Then, improved correlation filters using the enhanced features are built, the optimal functions of which can be effectively solved by Gauss-Seidel iteration. In addition, the average peak-response difference

SUBMITTER: Yang H 

PROVIDER: S-EPMC6479297 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature