关键词: 时空上下文, 漂移, Kalman滤波, 多示例学习, 实时性

Abstract: As traditional fast tracking via spatio-temporal context learning fails to track target stably when target is in the complex background and occlusion condition, a robust fast tracking via spatio-temporal context learning is proposed. The Kalman filter is used to estimate and predict the target’s position in the next frame of current frame. The estimated position is used as the starting point of the iteration of the fast tracking via spatio-temporal context learning in the next frame. Results of tests on variant video sequences show that the proposed algorithm has advantages over fast tracking via spatio-temporal context learning and multiple instance learning tracking when target is in the complex background and occlusion condition. Obtained results satisfy the requirements of real-time tracking.

Key words: spatio-temporal context, drift, Kalman filter, multiple instance learning, real-time