Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment.

翻译：数据稀缺已成为计算机视觉领域基于人工智能的监督模型发展的主要障碍之一。事实上，基于深度学习的方法在应用于训练中未见过的全新场景时总会遇到困难，且可能无法在非寻常但至关重要的现实世界情境中得到充分测试。本文提出并公开发布CrowdSim2——一个基于Unity图形引擎模拟器生成的、适用于行人与车辆检测的新型合成图像数据集。该数据集包含数千张从模拟真实世界的多种合成场景中采集的图像，其中我们改变了若干关键因素，如天气条件和场景中的目标数量。标注信息自动获取，由精确定位两个目标类别物体的边界框组成，且将人类排除在标注流程之外。我们利用这一新基准作为若干先进检测器的测试平台，结果表明我们的模拟场景可作为在受控环境中评估其性能的有力工具。