Abstract
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.
Highlights
Today, a number of cameras are installed in various devices such as smart phones, PCs, security devices, etc
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 angular error (AAE)) were evaluated and it was revealed that the proposed method improved the performance of optical flow calculation for various videos
One of the rapidly developing studies of computer vision and deep learning are the detection of moving objects [2] [3], where the most fundamental information is optical flow [4] [5] [6] that indicates corresponding points in two images
Summary
A number of cameras are installed in various devices such as smart phones, PCs, security devices, etc. One of the rapidly developing studies of computer vision and deep learning are the detection of moving objects [2] [3], where the most fundamental information is optical flow [4] [5] [6] that indicates corresponding points in two images. Optical flow is calculated based on two kinds of information, that is, edges and texture of objects. The previous research focused on how the accuracy of optical flow for videos containing silhouette images of animals can be improved [12]. A new method for improving the accuracy of optical flow for silhouette images is proposed in this paper. Focusing on silhouette images, seeking novelty to improve the accuracy of existing dense optical flow methods. The improvement was estimated by using a publicly available video dataset
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