针对监控视频中运动目标跟踪问题，提出了一种栅格划分的梯度朝向直方图特征对目标进行描述，并通过可视化技术来实时观测目标局部区域特征量的变化，通过变化来感知目标在运动中可能发生的遮挡等异常情况.利用感知到的状态自适应调整参加融合计算的子模板的个数，指导跟踪过程，以防止异常情况对跟踪结果的影响，实现了对视频目标准确、稳健的跟踪.实验结果表明，提出的目标局部特征能够较准确地感知目标及周围环境发生的变化，对于提高跟踪算法的鲁棒性和准确性具有积极的作用.

A grid-based histogram of the orientation gradient feature and visualization techinique were used in object tracking algorithm to solve the problem of tracking objects moving in surveillance video. The histogram of orientation gradient based on grid was designed as the local feature to describe the object. And it can apperceive the abnormalities of objects such as occlusion, disturbance and deformation etc. The visualization was used to real-time observe the changes of the characteistics through the changes of the local feature. The weights of the sub-model was adjusted adaptively to keep the stability of the tracking algorithm. The experimental results show that the proposed local feature can perceive the abnormity correctly, and can impove the robustness and stability of the tracking algorithm. The self-perception matching algorithm based on this local feature can tracking object accurately and stably in surveillance video.