提出一种基于SVM(Support Vector Machine)优化的TLD(Track-Learning-Detection)行人检测跟踪算法.将行人作为正样本，背景作为负样本，提取出行人的HOG特征并投入线性SVM中进行训练，得到行人检测分类器，并标定出目标区域，实现行人自动识别；然后在TLD算法的基础上对行人进行跟踪和在线学习，估计检测出的正负样本并实时修正检测器在当前帧中的误检，利用相邻帧间特征点配准剔除误配点，同时更新跟踪器数据，以避免后续出现类似错误.实验表明，该算法能够适应遮挡变化且自动识别并稳定跟踪目标行人，较传统跟踪算法具有更强的鲁棒性.
A new method based on optimized TLD (Track-Learning-Detection) and SVM (Support Vector Machine) for tracking pedestrian was proposed. First, with pedestrians as positive samples and the background as negative samples respectively, HOG (Histogram of Oriented Gradient) descriptor of pedestrian was extracted and combined with linear SVM to train the pedestrian classifier，which was used to obtain the calibrated pedestrian area accurately. Then, adaptive tracking and online learning on the pedestrians on the basis of TLD were integrated to estimate the reliability of the positive and negative samples, to rectify error existing in the current frame caused by detection and to update the tracking data simultaneously to avoid subsequent similar mistakes. The experiment results demonstrate that, compared with the conventional tracking algorithm, the proposed algorithm can not only significantly adapt to occlusions and appearance changes but also automatically identify and track pedestrian targets at arbitrary position, manifesting stronger robustness.