The Enrichment of Texture Information to Improve Optical Flow for Silhouette Image
The Enrichment of Texture Information to Improve Optical Flow for Silhouette Image
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DOI：
10.14569/ijacsa.2021.0120253
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发表时间：
2021
影响因子：
0.9
通讯作者：
Bedy Purnama;Mr. Kartika;Kunti Robiatul;Fatma Indriani;Mamoru Kubo;K. Satou
中科院分区：
文献类型：
--
作者：
Bedy Purnama;Mr. Kartika;Kunti Robiatul;Fatma Indriani;Mamoru Kubo;K. Satou
计算机视觉与机器学习的最新进展使视频数据中移动对象的检测、跟踪和行为分析成为可能。光流是这种计算的基本信息。因此，长期以来，人们一直希望能有准确的算法来正确地计算它。针对轮廓数据中存在边缘信息而不含纹理信息的问题进行了研究。针对目前主流的光流计算算法在解决这一问题上的不足，提出了一种光流计算方法。该算法通过在轮廓图像的内部用不同的颜色绘制收缩边缘，人为地丰富了轮廓图像的纹理信息。通过引入纹理信息，为主流的光流计算算法提供了一条计算更优光流的思路。通过使用DAVIS 2016数据集中的10个动物视频和TV-L1算法进行密集光流计算的实验，评估了两个误差值（MEPE和AAE），并显示所提出的方法提高了各种视频的光流计算性能。此外，实验结果还揭示了收缩边缘的大小与运动类型和运动速度之间的关系。关键词-光流;轮廓图像;人工增加纹理信息
Recent advances in computer vision with machine learning enabled detection, tracking, and behavior analysis of moving objects in video data. Optical flow is fundamental information for such computations. Therefore, accurate algorithm to correctly calculate it has been desired long time. In this study, it was focused on the problem that silhouette data has edge information but does not have texture information. Since popular algorithms for optical flow calculation do not work well on the problem, a method was proposed in this study. It artificially enriches the texture information of silhouette images by drawing shrunk edge on the inside of it with a different color. By the additional texture information, it was expected to give a clue of calculating better optical flows to popular optical flow calculation algorithms. Through the experiments using 10 videos of animals from the DAVIS 2016 dataset and TV-L1 algorithm for dense optical flow calculation, two values of errors (MEPE and AAE) were evaluated and it was revealed that the proposed method improved the performance of optical flow calculation for various videos. In addition, some relationships among the size of shrunk edge and the type and the speed of movement were suggested from the experimental results. Keywords—Optical flow; silhouette image; artificial increase of texture information