Numerous roadside perception datasets have been introduced to propel advancements in autonomous driving and intelligent transportation systems research and development. However, it has been observed that the majority of their concentrates is on urban arterial roads, inadvertently overlooking residential areas such as parks and campuses that exhibit entirely distinct characteristics. In light of this gap, we propose CORP, which stands as the first public benchmark dataset tailored for multi-modal roadside perception tasks under campus scenarios. Collected in a university campus, CORP consists of over 205k images plus 102k point clouds captured from 18 cameras and 9 LiDAR sensors. These sensors with different configurations are mounted on roadside utility poles to provide diverse viewpoints within the campus region. The annotations of CORP encompass multi-dimensional information beyond 2D and 3D bounding boxes, providing extra support for 3D seamless tracking and instance segmentation with unique IDs and pixel masks for identifying targets, to enhance the understanding of objects and their behaviors distributed across the campus premises. Unlike other roadside datasets about urban traffic, CORP extends the spectrum to highlight the challenges for multi-modal perception in campuses and other residential areas.

翻译：众多路侧感知数据集被提出以推动自动驾驶和智能交通系统研究的发展。然而，现有数据集大多聚焦于城市主干道，无意中忽略了公园、校园等具有截然不同特征的居住区域。针对这一空白，我们提出了CORP，这是首个专为校园场景下的多模态路侧感知任务设计的公开基准数据集。CORP在大学校园内采集，包含超过20.5万张图像和10.2万个点云数据，这些数据由18个摄像头和9个激光雷达传感器获取。这些不同配置的传感器安装在路边线杆上，提供校园区域内多样化的视角。CORP的标注涵盖超越2D和3D边界框的多维信息，为三维连续跟踪和实例分割提供额外支持，具体包括用于目标识别的唯一ID和像素掩码，从而增强对校园场所内分布的目标及其行为的理解。与其他关注城市交通的路侧数据集不同，CORP拓展了研究范畴，突出了校园及其他居住区域中多模态感知所面临的挑战。