多目标跟踪的改进Camshift/卡尔曼滤波组合算法

Combined Algorithm with Modified Camshift and Kalman Filter for Multi-object Tracking

  • 摘要: 针对多目标跟踪中,目标瞬间丢失、目标交错或重叠时目标跟踪失败等情况,提出了一种改进Camshift(continuously adaptive mean shift)算法和卡尔曼滤波组合的多目标跟踪方法.在Camshift算法中,从目标的颜色直方图模型得到每帧图像的反向投影图,根据目标的大小自适应地调整搜索窗口尺寸,并迭代计算各目标窗口的质心位置.通过自适应地扩展搜索窗口,从而解决了因目标加速度而引起的目标瞬间丢失问题.采用卡尔曼滤波实现对运动目标的位置估计,以克服多目标运动引起的交错或重叠以及噪声干扰.实验结果表明,这种组合算法能有效地改善多目标跟踪的性能,实现目标连续跟踪.

     

    Abstract: Aiming at several problems occurred in multi-object tracking,such as the moving objects interleaving or overlapping, and the object losing momentarily,a new multi-object tracking algorithm is proposed that combines the modified Camshift(continuously adaptive mean shift) algorithm with Kalman filter.In the Camshift algorithm,a back-projection image is obtained from the color histogram model of each image frame,the size of searching window is adjusted adaptively on the object's size,and the center position of window is computed iteratively.By extending the searching window'size adaptively,the issue of the object losing instantaneously from the current window for motion acceleration is solved.Moreover, Kalman filter is used for estimating the position of each moving object to overcome the moving objects interleaving or ovrelapping,and noise disturbance.The experimental results show that the proposed algorithm can improve the performance of multi-object tracking effectively,and realize keeping on multi-object tracking continuously.

     

/

返回文章
返回