显著度目标示性及背景自适应约束的目标跟踪算法*

针对目标受环境干扰和自身姿态变化引起的跟踪漂移和目标丢失等问题,提出显著度目标示性及背景自适应约束的目标跟踪算法.在粒子滤波跟踪框架中,首先根据贝叶斯显著度分别对目标区域和扩展目标区域内的像素特征加权,构建目标的示性模型.再根据背景区域的显著度,自适应地选择背景区域约束跟踪过程.最后根据目标当前的外观状态,利用目标与背景之间的关联性得到跟踪结果.文中算法的显著度目标示性模型降低目标匹配中的误差,自适应背景约束提高目标受到遮挡或姿态发生变化时的跟踪准确性.实验表明,文中算法具有较强的跟踪鲁棒性和较高的跟踪准确率.

Object Tracking Algorithm Based on Object Saliency and Adaptive Background Constraint

To seek a solution of tracking drift and object loss resulted from environmental interference and appearance change of the object, a object tracking algorithm via object saliency and adaptive background constraint is proposed. In the tracking framework of particle filter, the pixel characteristics of the object and the extended object are firstly weighted to construct the explicit model of the object according to the principle of Bayesian saliency. Next, the background around the object is considered adaptively by exploiting the saliency of the background. Finally, by judging the current appearance state of the object, the tracking result is obtained by taking advantage of the correlation between the object and the background. Matching error is reduced by the object saliency model, while tracking accuracy is improved by the adaptive constraint of background with occluded object and changed pose. The experimental results demonstrate the proposed method with stronger robustness and higher precision for object tracking.