Motion Estimating Optical Flow for Action Recognition : (FARNEBACK, HORN SCHUNCK, LUCAS KANADE AND LUCAS-KANADE DERIVATIVE OF GAUSSIAN)

Rosepreet Kaur Bhogal, V. Devendran
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Abstract

Motion estimating is one of the methods which determines the movement from one frame to another in the videos. For an application of action recognition, choosing the optical flow can be an essential feature for recognizing actions. The optical flow consists of the information of the moving subject and objects in the video frames. This paper analyzes four motion estimating optical flow methods (Farneback, Horn Schunck, Lucas Kanade, and Lucas-Kanade Derivative of Gaussian explored based on visualization and PSNR. The NTURGB+D dataset uses for the analysis of experimental results.
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用于动作识别的运动估计光流:(FARNEBACK, HORN SCHUNCK, LUCAS KANADE和LUCAS-KANADE高斯导数)
运动估计是确定视频中从一帧到另一帧的运动的方法之一。在动作识别的应用中,选择光流是动作识别的一个重要特征。光流由视频帧中运动主体和物体的信息组成。本文分析了基于可视化和PSNR的四种运动估计光流方法(Farneback、Horn Schunck、Lucas Kanade和Lucas-Kanade Gaussian导数)。NTURGB+D数据集用于分析实验结果。
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