Learning large margin support correlation filter for visual tracking

Abstract

Correlation filter-based tracking methods realize dense sampling via the circular shift of an image patch, thereby improving the generalization ability of the trackers. However, the discriminative power of these methods is still limited due to the boundary effect and ridge regression framework. Instead of a traditional correlation filter, a classifier is learned in the form of a support correlation filter. Aiming at the enhancement of the discriminative power, the support correlation filter learning on the training samples is accomplished under the large margin framework. Furthermore, a cropping operation is implemented on each feature map to extract the central part of the feature as a reliable training sample. In particular, this operation is implicitly carried out through the integration into the learning framework. An alternating optimization algorithm is further developed to obtain the solution for the large margin support correlation filter. The experimental results on the benchmark dataset demonstrate the superior performance of the method.