A Visual Tracking Algorithm Based on Visual Saliency and Multiple Features Fusion

Recent years, Discriminant Correlation Filter(DCF) has shown great advantages in the field of visual tracking, however its potential was greatly limited due to using single-resolution feature maps when applied to video with background interference. Therefore, this paper firstly used visual saliency to eliminate extra background information and outstand the object, and then extracted several features from different resolution images and merged them into a new feature vector. Then, the feature is used to train correlation filter templates for tracking. Compared with traditional algorithms, the proposed algorithm performed well on 51 benchmark videos of OTB. The method was robust to against challenges such as lighting changes, scale changes, occlusion, motion blur and while running at hundred frames-per-second, and was superior to other algorithms in distance accuracy and success rate.