基于熵背景减法和CAMShift的运动目标路径跟踪

C. Arpitha, M. R. Sunitha
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引用次数: 2

摘要

对于多目标路径跟踪,我们将熵背景减法和CAMShift算法相结合。首先利用克劳修斯熵理论将图像域中的每个像素点变换到熵域中,得到其能量等级。然后利用熵背景相减算法检测每帧中的运动目标区域。为了提高目标颜色不同情况下的鲁棒性,目标颜色与背景颜色相同。通过选择每个目标区域来实现目标的定位。该方法利用CAMShift算法的多跟踪器实现交通视频中运动车辆的自动标定和多目标跟踪。该系统还能够跟踪移动车辆的路径。结果和分析表明,本文所采用的方法有效地解决了视频序列中的多目标自动跟踪问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Moving objects Path Tracking based on Entropy Background Subtraction and CAMShift
For multiple object path tracking we integrate entropy background subtraction method and CAMShift algorithm. Initially claussius entropy theory is used to transform each pixel in the image domain into entropy domain and obtain its energy level. Later we use entropy background subtraction algorithm to detect moving object region in each frame. To improve robustness in the condition where objects are of different color, object colors are same as to background’s colors. localization of object is obtained by choosing each objective region. This approach can automatically calibrate moving vehicles in traffic video and achieve multi-target tracking by using a multi-tracker of CAMShift algorithm. The proposed system is also capable of tracing paths of moving vehicles. The results and analysis demonstrates that the methods used in the paper finds solution for automatic multi-object tracking problems in video sequence efficiently.
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