连续同色调背景下运动目标自适应跟踪

ADAPTIVE MOVING TARGET TRACKING UNDER INCESSANT SAME HUE BACKGROUND

针对传统的Meanshift算法在连续的同色调背景干扰下无法准确、持续跟踪目标的问题,提出一种基于Kalman-optical flow(KOF)的改进Meanshift目标跟踪算法。首先,通过基于色调空间的光流检测对Meanshift窗口区域内的特征点进行建模,获得其图像坐标;然后,利用Kalman滤波的速度预估排除背景特征点,得到基于目标模型特征点的空间约束条件;最后,将得到的空间约束条件结合传统Meanshift算法中的色调约束条件,构建新的反投影直方图,并将新的反投影图作为Meanshift的概率密度图进行迭代,从而完成目标跟踪。实验表明,在连续的同色调背景区域的干扰下,该算法仍可以准确、持续地跟踪目标。

Aiming at the problem that traditional Meanshift algorithm cannot track the target accurately and constantly under the interference of incessant same hue background,we proposed an improved Meanshift target tracking algorithm which is based on Kalman-optical flow( KOF).First,it models the feature points within the area of Meanshift window by hue space-based optical flow detection algorithm,and gets the coordinates of their images. Secondly,it uses the speed prediction of Kalman filter to exclude background feature points and obtains the space constraint condition which is based on the target model feature points. Finally,it combines the derived space constraint condition with the hue constraint condition of traditional Meanshift algorithm to construct new back projection histogram,and uses the new back projection graph as the probability density map of Meanshift for iteration,so as to complete the target tracking. Experimental results demonstrated that under the interference of incessant same hue background area this algorithm could still track targets accurately and constantly.