在目标尺寸和颜色发生变化时，传统均值漂移法因目标模型单一和核窗口大小方向固定而导致目标丢失． 为此，文中提出一种基于多特征概率分布的均值漂移行人跟踪
算法，首先利用目标的颜色、轮廓和运动特征构建目标模型，得到颜色、边缘和运动直方图分布; 然后将颜色和边缘的直方图反向投影生成二维概率密度分布，利用运动信息修正颜色和边缘概率分布; 并根据各特征所占权重，运用自适应融合法得到目标特征关联概率分布; 最后利用关联概率密度的零阶矩值调整下一帧跟踪窗口尺寸，结合均值漂移跟踪框架，实现常态下目标跟踪． 实验结果表明，该算法提取的目标特征具有较强的准确性，能实现复杂交通场景下的行人跟踪．

For the traditional mean shift tracking algorithm,single feature and fixed nuclear window size may result in a track loss when the size and color of targets change.In order to solve this problem,a pedestrian tracking algorithm is proposed based on the multi-feature probability distribution and the mean shift.In the algorithm,first,a target model is constructed based on the color,outline and movement features,and thus the color,edge and movement histogram distributions are obtained.Then,a two-dimensional probability density distribution is created by means of the back-projection of the color and edge histograms,and the color and edge probability distributions are corrected by using the movement information.Moreover,according to the multi-feature weights,the correlation probability distribution of target features is achieved by the adaptive fusion method.Finally,the zero moment of the correlation probability distribution is used to adjust the size of the next tracking window,and by combining the mean shift tracking framework,a normal target tracking is realized.Experimental results indicate that the proposed algorithm is more accurate in extracting target features and can track pedestrians in complex traffic scenes.