Robust visual tracking by metric learning with weighted histogram representations
Measuring the similarity between the target template and a target candidate is a critical issue in visual tracking. An appropriate similarity metric can improve the accuracy and robustness of visual tracking. This paper proposes a robust visual tracking algorithm that incorporates online distance metric learning into visual tracking based on a particle filter framework. The appearance variations of an object are effectively learned via an online metric learning mechanism. In addition, we use spatially weighted feature representations using both color and spatial information of objects, which can further improve the tracking performance. The proposed algorithm is compared with several state-of-the-art tracking algorithms, and experimental results on challenging video sequences demonstrate the effectiveness and robustness of the proposed tracking algorithm.
visual tracking weighted histogram representations distance metric learning particle filters