基于相关系数的相关跟踪算法研究

提出了一种以相关系数作为相似度度量准则的相关跟踪算法，克服了传统的以点对点乘累加作为相似度度量准则的相关跟踪算法跟踪精度低的缺点。给出了相似性度量的快速实现方法，解决了目标跟踪的实时性要求。同时．还提出了一种新的模板更新策略，使得跟踪算法对环境的适应能力和稳定性得到较大的提高。此外，提出了跟踪失败判决策略，解决了因目标暂时消失或环境突然变化，如瞬间明暗变化，造成的成像质量差而引起的跟踪失败。试验结果表明，该相关跟踪算法减少了相关跟踪的复杂度，具有跟踪精度高和速度快的特点。目前，该算法已经应用在实时目标跟踪系统中。

Research of Correlation Tracking Algorithm Based on Correlation Coefficient

This paper presents a correlation tracking algorithm based on correlation coefficient, which overcomes the disadvantages of traditional correlation tracking based on point-to-point multiple with accumulation and has the advantages of good accuracy and high stability. At the same time, many measures are put forward to improve the speed of the algorithm, which resolve the requirement of real-time of object tracking. And during the object-tracking, there may be many changes in a sequence of the object images, therefore, a reasonable strategy of template updating will be the key of the object-tracking problem. On the basis of the similarity measurement and template buffers, a suitable template updating strategy is given, which effectively decreases the accumulation of the object-tracking error, and greatly improves the stability of the object-tracking. The failure judgement of object-tracking presented effectively resolves the transitory failure of tracking, caused by sudden changes, such as the changes between the dark and the bright, or the tracked object is covered temporarily. The experiments show the solution decreases the complex of correlation tracking, and has the advantages of good accuracy and high speed as well. Now this algorithm has been applied to real-time object-tracking system.