为满足光电跟踪系统图像跟踪抗遮挡、实时性的要求，提出了一种改进检测器和目标模型更新策略的 TLD 算法。首先，通过帧差法获得差分图像序列，其次，利用动态 Otsu 阈值对差分图像进行二值化处理，滤除背景差分像素，获取移动物体边界框，最后，产生局部滑动窗口，进行随机厥分类和最近邻分类；并且优化了目标模型更新策略。实验表明，对于分辨率为320×240的视频，改进算法较原算法跟踪速度提升比平均为1.50，满足系统的实时性要求；改进算法抗遮挡性能及在低对比度环境中的跟踪性能优于 Mean-Shift 算法，满足系统的抗遮挡要求。%To meet the requirements of real-time and anti-occlusion tracking in optoelectronic tracking systems, an improved Tracking-Learning-Detection (TLD) approach with a modified detector and a modified strategy of module updating is proposed. Firstly, difference images are obtained by the difference between consecutive frames. Then, binary images are obtained by an adaptive Otsu threshold. As a result, difference pixels of background part are filtered and moving object bounding boxes are obtained from the binary images. Finally, local scanning-windows are generated for Ensemble Classifier and Nearest Neighbor Classifier. And the strategy of module updating is improved. The experimental results indicate that the average speedup per frame of the improved TLD approach reaches up to 1.50. Compared with the traditional TLD, the improved approach can meet system’s real-time requirement tracking performance of the improved TLD approach under occlusions or in low contrast environment is better than the Mean-Shift approach, and the improved approach can meet system’s requirement of anti-occlusion tracking.
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