High-speed tracking with multi-templates correlation filters

Abstract

Correlation filter (CF)-based trackers have demonstrated impressive performance on visual tracking benchmark datasets with high frame rates. A lot of research focuses on extracting strong features to learn a robust model of the object’s appearance. However, the circularly shifted samples used in traditional CF training come from one single image patch and have limited information about the target. Inspired by various data augmentation methods in deep learning, we propose a general multi-templates correlation filter-based tracker that expands the CF tracker with multihandcrafted training samples. We reformulate the original optimization problem and provide a closed-form solution to maintain the high-speed calculation. Several methods to generate the additional samples are evaluated. Comprehensive experiments illustrate that our framework obviously improves the tracking performance and is general enough to be incorporated with classical CF trackers.