基于加权后投影CAMShift的实时非刚性目标跟踪

Lei Sun, Bingrong Wang, T. Ikenaga
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引用次数: 4

摘要

连续自适应均值移位算法(CAMShift)是对均值移位算法的一种改进,适用于目标跟踪,特别是头部和面部跟踪。传统的CAMShift无法处理多色目标跟踪和附近存在相似颜色的情况。本文提出了一种新的解决这些问题的方法——带加权反投影的CAMShift。在我们的方法中,使用了带有阈值策略的多维直方图。针对被跟踪目标附近存在相似颜色的情况,提出了一种新的反投影加权策略。实验结果表明,该方法在保持处理速度实时性的前提下,在多色目标或相似色背景的情况下优于传统的CAMShift方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time Non-rigid Object Tracking Using CAMShift with Weighted Back Projection
The Continuously Adaptive Mean Shift algorithm (CAMShift) is an adaptation of mean shift algorithm for object tracking especially for head and face tracking. Traditional CAMShift can not deal with multi-colored object tracking and situations when similar colors exist nearby. In this paper, a new approach towards these problems using CAMShift with weighted back projection is proposed. In our approach, multi-dimensional histogram with thresholding strategy is utilized. And a new back projection weighting strategy is proposed for situations when similar colors exist near the tracked object. Through experiments, the results show that the proposed method exceeds the traditional CAMShift in situations with multi-colored object or similar-colored background while keeping the processing speed real-time.
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