A tracking algorithm based on improved Camshift and UKF is proposed in this paper to deal with the problems which exist in traditional Camshift algorithm, such as artificial orientation and tracking failure under colo...A tracking algorithm based on improved Camshift and UKF is proposed in this paper to deal with the problems which exist in traditional Camshift algorithm, such as artificial orientation and tracking failure under color interference as well as object’s changed illumination occlusion. Meanwhile, in order to solve the sheltered problem, the UKF is combined with improved Camshift algorithm to predict the position of the target effectively. Experiment results show that the proposed algorithm can avoid the interference of the background color and solve the sheltered problem of the object, so that achieving a precise and timely tracking of moving objects. Also it has better robustness to color noises and occlusion when the object’s scale changes and deformation occurs.展开更多
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...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.展开更多
文摘A tracking algorithm based on improved Camshift and UKF is proposed in this paper to deal with the problems which exist in traditional Camshift algorithm, such as artificial orientation and tracking failure under color interference as well as object’s changed illumination occlusion. Meanwhile, in order to solve the sheltered problem, the UKF is combined with improved Camshift algorithm to predict the position of the target effectively. Experiment results show that the proposed algorithm can avoid the interference of the background color and solve the sheltered problem of the object, so that achieving a precise and timely tracking of moving objects. Also it has better robustness to color noises and occlusion when the object’s scale changes and deformation occurs.
文摘针对多目标跟踪中,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.
