Multi-invariance appearance model for object tracking

Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on intensity information, texture information, or use simple color representations for image description, which cannot provide allaround invariance to different scene conditions. Meanwhile there exists no single tracking approach that can successfully handle all scenarios. Due to the complexity of the tracking problem, the combination of multiple features should be computationally efficient and possess a certain amount of robustness while maintaining high discriminative power. This paper combine intensity information (cross-bin distribute field, CDF), texture information (enhance histograms of oriented gradients, EHOG) and color information (color name, CN) in a tracking-bydetection framework, in which a simple tracker called CSK is extended for multi-dimension and multi-cue fusion. The proposed approach improves the baseline single-cue tracker by 4.4% in distance precision. Furthermore,we show that our approach achieving 75.4% is better than most recent state-of-the-art tracking algorithms.