Adaptive Learning Compressive Tracking Based on Kalman Filter

Object tracking has theoretical and practical application value in video surveillance, virtual reality and automatic navigation. Compressive tracking(CT) is widely used because of its advantages in accuracy and efficiency. However, the compressive tracking has the problem of tracking drift when there are object occlusion, abrupt motion and blur, similar objects. In this paper, we propose adaptive learning compressive tracking based on Kalman filter (ALCT-KF). The CT is used to locate the object and the classifier parameter can be updated adaptively by the confidence map. When the heavy occlusion occurs, Kalman filter is used to predict the location of object. Experimental results show that ALCT-KF has better tracking accuracy and robustness than current advanced algorithms and the average tracking speed of the algorithm is 39 frames/s, which can meet the requirements of real-time.

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