The Enrichment of Texture Information to Improve Optical Flow for Silhouette Image

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