Target tracking algorithm based on improved kernel correlation filtering

Abstract

Aiming at the problem of severe accuracy degradation in the traditional KCF (Kernel correlation filter) target tracking algorithm in a complex environment, we propose an improved kernel correlation filter target tracking algorithm. First, the target scale is estimated by fusing the scale filter in the DSST (Discriminative Scale Space Tracking) algorithm to solve the traditional KCF algorithm cannot estimate the scale. Secondly, So as to solve the KCF algorithm's limited ability to express the features of the target appearance model, by fusing HOG (Histogram of Oriented Gradient) features and CN (Color Name) features, the algorithm's ability to extract target features is enhanced. Finally, by introducing peak response fluctuation detection, the credibility of the tracking target is judged, so as to decide whether to update the filter model. The experiment tested the overall tracking performance of the improved KCF algorithm by using the video sequences in the OTB2013 and OTB2015 standard data sets. The experimental results show that the accuracy and performance of the improved algorithm in complex environments have been significantly improved.