中文摘要：

针对基于检测的目标跟踪算子存在着目标表观描述的准确性和对跟踪过程的目标表观变化的适应性之间的矛盾,提出一种较为柔性的半监督学习方法：以Hough Forests为基本学习框架,用离线学习的分类器作为先验跟踪算子,并引入基于跟踪过程中用检测得到的object-specific信息和空间一致性信息的随机标签分布,用来生成半监督学习所需要的在线数据样本,对跟踪算子进行修正;分类器的检测和跟踪环节与在线的Hough Forests分类器应用环节类似,但采用了类似粒子滤波的随机采样方式对检测和更新环节进行加速;用匀速运动模型对目标运动进行建模,使跟踪过程中的目标位置预测更加准确.算法在标准跟踪数据集合i-Lids和TUD-campus上与当前流行的目标跟踪算法在线Boosting算法、MILB和Hough Forests跟踪算法进行了比较实验.实验证明：监督Hough Forests分类器提供了解决分类器对目标表观表述的准确性和自适应性矛盾的一种有效机制;整个跟踪算法能够使目标跟踪过程更加快速、鲁棒与准确.

英文摘要：

State-of-art tracking-by-detection methods have somewhat conflict between accuracy of object appearance description and adaptivity to object appearance changing in tracking process.Focused on this difficult problem,a flexible semi-supervised learning method based on Hough Forests classifier was proposed.First,an off-line classifier as prior tracker for object appearance description was used.Then,a random label distribution,which is based on the spatial consistency in context and the object-specific information detected in tracking,was introduced to generate on-line examples for updating semi-supervised on-line classifier.Detection and updating phases were carried out in a similar way as normal on-line Hough Forests classifier goes,however,a particle-filtering-kind random sampling scheme was implemented in detection and updating phases to accelerate tracking.Finally,a uniform motion model was employed to help object position prediction more accurate.Experiments were carried out on standard visual tracking databases such as i-Lids and TUD-campus,and the results were compared with those of the popular tracking algorithms such as on-line Boosting,MILB and Hough Forest tracking.Experimental results show that this semi-supervised Hough Forests classifier provides an optimal fashion to efficiently solve the accuracy/adaptivity conflict of classifier;meanwhile,the proposed tracking algorithm can significantly improves the tracking performance in tracking rate,robustness and precision.