TLD(tracking-learning-detection)是近期受到广泛关注的一种有效的视频目标跟踪算法。在原始TLD的基础上,对其进行改进,改进包括:在TLD的跟踪器中对其局部跟踪器的布置和局部跟踪器的跟踪成败预测方法进行改进,提高跟踪器的跟踪精度和鲁棒性;在TLD的检测器中引入基于Kalman滤波器的当前帧目标所在区域预估,缩小了检测器的检测范围,提高了检测器处理速度;在TLD的检测器中加入基于马尔可夫模型的方向预测器,增强了检测器对相似目标的辨识能力。通过实验对原始TLD和改进后的TLD进行了比较,实验结果显示改进后的TLD算法较原始TLD算法具备更高的跟踪精度和更快的处理速度,而且增强了对场景中相似目标的辨识能力。

As an effective visual target tracking algorithm, the tracking-learning-detection(TLD) has drawn wide attention around the world. In this paper, we propose an improved TLD visual target tracking algorithm, which is obtained by making several improvements based on the original TLD algorithm. The improvements include modifying local tracker placement as well as local tracker failure predicting method for the tracker of TLD to improve the precision and robustness of the tracker; employing Kalman filter in the detector of TLD for estimating the location of the target to reduce the scanning region of the detector and improve the speed of the detector; adding Markov model based target moving direction predictor in the detector of TLD to increase the discretion for targets with similar appearance. Experiments have been conducted to compare the performance of the original TLD and improved TLD. The experimental results show that, compared with the original TLD, the improved TLD has more accurate tracking precision, faster tracking speed and stronger distinguishing ability for targets with similar appearance in the scene.