In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.
There have been many attempts that use virtual world to carry out scientific researches. Bainbridge @cite_20 investigated the feasibility of utilizing Second life and World of Warcraft as sites for research in the social, behavioral, and economic sciences, as well as computer science. With a virtual living lab, Prendinger @cite_19 conducted several controlled driving and travel studies.
@cite_19 @cite_20
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Online virtual worlds, electronic environments where people can work and interact in a somewhat realistic manner, have great potential as sites for research in the social, behavioral, and economic sciences, as well as in human-centered computer science. This article uses Second Life and World of Warcraft as two very different examples of current virtual worlds that foreshadow future developments, introducing a number of research methodologies that scientists are now exploring, including formal experimentation, observational ethnography, and quantitative analysis of economic markets or social networks. This issue includes two articles with additional research examples from distinguished experts in social science and computer science. In the first article, "Secondary Avatars and Semiautonomous Agents" presents semiautonomous agent assistants that have been incorporated into recent MMOGs. In the second article, a group of people constructed combat, mentoring, and trust networks based on data from Sony's popular MMOG Everquest II. They also provide an excellent summary of their ongoing research in performance and learning, player churn analysis, and identifying undesirable behavior in MMOGs. The authors argue that the close collaboration between social scientists and computer scientists is creating an emerging area called "computational social science," where computation is used as an integral mechanism in social science research.