关键词: 视频序列, 目标跟踪, 均值漂移跟踪, 尺度估计, 探测器, 粒子滤波

Abstract:

In real-time target tracking of video sequence, aiming at the problem that the classical Mean Shift Tracking（MST） method can’t deal with occlusion and scale change, a tracking method combining MST, self-learning detector and particle filter is proposed. Firstly, the MST algorithm is used to track the object in the video frame, and the object is re-initialized when the object converges to the local minimum. Then, a detector based on online learning is proposed to update the object model of MST adaptively, so that it can automatically adjust the object scale. When complete occlusion occurs, the particle filter is activated to estimate the object position by probability calculation, so that MST can recover tracking when the object leaves occlusion. The experimental results on PETS video sequence datasets show that compared with several existing MST methods, this method has high tracking accuracy and can be used in real-time detection and object tracking applications.

Key words: video sequence, object tracking, mean shift tracking, scale estimation, detector, particle filtering