计算机科学 ›› 2015, Vol. 42 ›› Issue (10): 287-291.
郑远力,胡志坤
ZHENG Yuan-li and HU Zhi-kun
摘要: TLD(Tracking-Learning-Detection)算法是近期广受关注的单目标长期跟踪算法。该算法由跟踪器、检测器、学习器协同工作,解决了目前大部分跟踪算法在目标丢失后不能重新识别目标的问题。但是由于检测器的计算量很大,该算法的实时性较差。针对这个问题,提出了一种动态生成检测扫描框的方法。输入的图片先采用跟踪器的前后向金字塔光流法加以计算,估计出目标的大概位置。然后在该位置区域生成滑动扫描框来检测。该方法可以有效缩小检测区域,减少检测器的计算量。将改进后的算法与原始算法以及Camshift、CT(Compress Tracking)算法进行了比较实验。结果表明,对于实时摄像头监控,改进的算法比原始算法具备更快的跟踪速度和更高的跟踪准确率。对于固定的图像序列,改进的算法的精度和速度都超过Camshift、CT算法。
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