Template matching by means of correlation is common practice in object tracking. However, its sensitivity to deformations of the patterns and the broad unsharp peaks are significant drawbacks. To reduce the influence of the noise and occlusion, a novel distance measurement method is proposed, in which first order statistics of the close points between the template and the image are used as a matching measurement. Instead of computing the distance of the gray value of the two images, it counts the number of the close pixel pairs in the object image and the template. The correlation curve got in this way is proved to be much sharper and the matching precision is improved significantly. When above method is applied to the processing of tracking, the reasonable update of the template will be the key. Based on above similarity measurement method, an adaptive template updating strategy is also presented, which is capable of selectively updating the correlation template in response to the changes in an image sequence and enable the correlation to track target under serious background clutter while decreasing the risk of template drift.