基于Meanshift算法和运动矢量分析的目标跟踪算法

Gang Tian, Rui-min Hu, Zhong-yuan Wang, Li Zhu
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引用次数: 18

摘要

均值移位算法在目标跟踪过程中不使用目标的运动方向和速度信息。当目标速度如此之快时,它很容易无法跟踪目标。为此,本文提出了一种将Mean shift算法与运动矢量分析相结合的目标跟踪算法。通过对视频编码过程中得到的运动矢量进行统计分析,得到目标的运动方向和运动速度,从而对Mean shift运动候选区域的中心点进行校正,使搜索位置更接近目标的实际中心。该方法既能有效跟踪快速运动目标,又能减少迭代收敛次数,提高运算效率。该算法已应用于我国智能视频监控设备中,实现了视频编码和目标跟踪在一个芯片上完成,实验结果表明了该算法的可行性和有效性。
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Object Tracking Algorithm Based on Meanshift Algorithm Combining with Motion Vector Analysis
Mean shift algorithm doesn't use the target’s motion direction and speed information in process of object tracking. When the target’s speed is so fast it easily fails to track the target. So a new object tracking algorithm combining Mean shift algorithm with Motion Vector analysis is proposed in this paper. By statistical analysis of the motion vector get from video encoding process, we can get the motion direction and velocity of target, which can be used to correct the central point of the motion candidate region of Mean shift, making the search position is more close to the actual centre of the target. This method can not only track the fast moving target effectively, but also reduce the number of iterative convergence times to improve the efficiency of operations. The algorithm is already use in our intelligent video surveillance equipment in which the operation of video encoding and object tracking is executed in one chip, and the experimental results show that it is feasible and effective.
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