基于多特征融合的均值偏移目标跟踪方法

An Improved Mean-shift Algorithm for Target Tracking Based on Multifeature Fusion

针对灰度图像目标色彩信息贫乏从而易陷于局部相似,致使跟踪点发生漂移导致跟踪失败的问题,构建了一种基于均值偏移的改进算法,在直方图模式中加入了目标对比度均值差分、平均梯度强度及局部灰度概率等特征,增加了目标特征维数,对目标进行精细刻画。并结合粒子滤波,有效提高灰度成像目标的跟踪稳定性和精确定位问题。试验结果表明,这种方法能够在较复杂背景下及目标快速运动时对锁定目标进行有效的跟踪定位,跟踪误差小,鲁棒性较强。

An improved algorithm based on mean-shift for target tracking is proposed in order to overcome the problem of tracking point shift, which comes from partial similarity between target and its background due to lack of color information in the gray images. The contrast mean difference feature, average gradient feature and gray level probability feature of the target are added to the target histogram. The particle filtering is applied to enhance the sta？bility and accuracy for tracking the target in the gray images. The experiment shows that the algorithm can better adapt to the complex background of the moving target, and improves the robustness and accuracy of the algorithm.