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Exhaustive search in local area around the object is used for tracking in the conventional Haar-like feature based on-line boosting tracking methods (HBT). Tracking is frequently failed and can hardly find the object again when the object moves fast or occluded completely. In addition, on-line tracking will gener- ate cumulative errors during updating when the state of the object and the envi- ronment changes. In this paper, we propose a fast on-line boosting tracking algo- rithm based on cascade filters of multi-features. Algorithm generates detection windows with various positions and sizes during initialization. Cascade detection screenings possible object windows by using multi-features: the manner of object motion, variance of detection windows, Object Similarity Statistic (OSS) model, color distribution and overlap ratio in turn. The left candidate object windows are passed to the strong classifier of on-line boosting and the object is located finally. In order to exert the advantage of boosting, naive Bayes classifier is used to re- place the original simple threshold classifier in HBT. Experiment results on vari- ous videos show that the robustness, accuracy and real-time ability are improved.

A fast on-line boosting tracking algorithm based on cascade filter of multi-features