A Multi-feature Fusion-based Algorithm for Real-time Single Object Tracking

A multi-feature fusion-based algorithm was proposed for real-time single object tracking by using the bilateral weighted least squares fuzzy support vector machine. In the proposed algorithm, the bilateral weighted least squares fuzzy support vector machine was trained with local HOG feature and global color feature respectively, and object tracking was achieved by using the linear combination of the two classifiers. For the local HOG feature-based classifier, the multiple base samples based correlation filtering was adopted to overcome matrix inversion. For the global color feature based-classifier, the unique thermal coding were used to encode the feature to achieve fast calculation. The experimental results on the public data sets show that compared with the state-of-the-art trackers, the proposed algorithm shows better tracking performance in deformation, fast motion, motion blur, and in-plane/out-of-plane rotation. © 2019, Editorial Department, Journal of South China University of Technology. All right reserved.