Because of the low contrast

lack of color

and low dynamic range of infrared images

the object tracking based on infrared imaging is rather difficult.An infrared object tracking algorithm is proposed by integrating the gray kernel histogram and SURF (speeded up robust features)features. An object template is represented by gray kernel histogram and SURF features in the first frame. The Mean Shift algorithm is used to find the suboptimal position rapidly in the next frame. Because the gray histogram contains less information

the tracking error is accumulated. Then

the improved SURF feature matching algorithm is used to estimate the size and center point of the current frame. The cumulative errors are amended to avoid the tracking window drifting gradually away from the object and the size of tracking window can be self-adapted. Finally

the object template is updated. Experimental results on real situations demonstrate that the proposed algorithm can track objects well in real-time ever when the appearance changes and similar apparents are existing around the targets.