Testing dataset for head segmentation accuracy for the algorithms in the ‘BGSLibrary’ v3.0.0 developed by Andrews Sobral - Loke SC; MacDonald BA; Parsons M; Wünsche BC (Elsevier B.V., 2020)
This dataset consists of video files that were created to test the accuracy of background segmentation algorithms contained in the C++ wrapper ‘BGSLibrary’ v3.0.0 developed by Andrews Sobral. The comparison is based on segmentation accuracy of the algorithms on a series of indoor color-depth video clips of a single person's head and upper body, each highlighting a common factor that can influence the accuracy of foreground-background segmentation. The algorithms are run on the color image data, while the ‘ground truth’ is semi-automatically extracted from the depth data. The camera chosen for capturing the videos features paired color-depth image sensors, with the color sensor having specifications typical of mobile devices and webcams, which cover most of the use cases for these algorithms. The factors chosen for testing are derived from a literature review accompanying the dataset as being able to influence the efficacy of background segmentation. The assessment criteria for the results were set based on the requirements of common use cases such as gamecasting and mobile communications to allow the readers to make their own judgements on the merits of each algorithm for their own purposes.
Keywords: Background initialization; Background maintenance; Background modeling; Background subtraction; Foreground detection; Foreground segmentation; Portrait segmentation