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Periodic motion detection with ROI-based similarity measure and extrema-based reference-frame selection

机译:具有基于ROI的相似性度量和基于极值的参考帧选择的周期性运动检测

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This paper presents a new algorithm for detecting and analyzing the periodic motions in video sequences. Different from the previous methods which detect periodic motions from the entire frame, we propose a convex-hull-based process to automatically determine the regions of interest (ROI) of the motions and utilize an ROI-based similarity measure to detect the motion periods. Furthermore, we also propose an extrema-based method to select the optimal reference frame for further improving the periodic detection performance. Our proposed algorithm can not only effectively detect motion periods with both constant and variable period lengths, but also have obvious advantage when handling periodic motion with slight movements. Experimental results demonstrate the effectiveness of our proposed method.
机译:本文提出了一种检测和分析视频序列中周期性运动的新算法。与从整个帧中检测周期性运动的先前方法不同,我们提出了一种基于凸包的过程来自动确定运动的关注区域(ROI),并利用基于ROI的相似性度量来检测运动周期。此外,我们还提出了一种基于极值的方法来选择最佳参考系,以进一步提高周期性检测性能。我们提出的算法不仅可以有效地检测具有恒定和可变周期长度的运动周期,而且在处理轻微运动的周期性运动时也具有明显的优势。实验结果证明了我们提出的方法的有效性。

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