CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks
This dataset addresses a gap for researchers in autonomous driving and intelligent transportation by focusing on campus scenarios, though it is incremental as it extends existing roadside dataset efforts to a new domain.
The authors tackled the lack of roadside perception datasets for residential areas like campuses by introducing CORP, a multi-modal dataset with over 205k images and 102k point clouds from a university campus, providing annotations for 3D tracking and instance segmentation.
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.