An Object Tracking Algorithm Based on the "Current" Statistical Model and the Multi-Feature Fusion

Aimed at accurary and real-time object tracking under complex background,an object tracking algorithm based on multi feature fusion is proposed. Feature points tracking is used to reduce the match time and improve the real-time of tracking; To overcome the inaccuracy of a single feature tracking, the object model is presented by the color and texture features. For the traditional "current" statistical model in maneuvering object tracking defects, an improved algorithm which combined with adaptive kalman filter (AKF) is proposed to improve the tracking accuracy. Experimental results show that the proposed method is effective and robust under complex background, the object is similar to each other, the target was partial occlusion and when the object is maneuvering. Index Term— object tracking; objective model; “current” statistical model; feature points; feature fusion