Multi Cue Adaptive Correlation Filters for Visual Tracking
Visual object tracking is a fundamental task in many high level computer vision applications. Most existing algorithms have to build complex models with expensive computation to achieve accurate object tracking, which brings significant difficulty in real time tracking. In order to address this problem, motivated by recent success of high speed correlation filter (CF) models, a novel real time object tracking method is proposed in this paper based on multi cue adaptive correlation filters (MCF) Different from conventional CF based trackers that employ a fixed feature, our method explores multi cue adaptation that automatically discovers the effective cues to handle specific scenarios. Specifically, our framework is built with multiple CF based trackers using different cues, such as intensity, color, texture and edge. In a new frame, the trackers run their own tasks simultaneously and compete to locate the target. A unified measure is introduced to evaluate the tracking performance of different trackers and find out the best prediction, which will be used as the final result of all the trackers. The proposed method is evaluated with extensive experiments both quantitatively and qualitatively, demonstrating that it is robust to various challenging factors in object tracking and outperforms many state of the art methods.
2016 6th International Conference on Digital Home (ICDH)