CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception
  Tasks
Beibei Wang Lu Zhang Shuang Meng Chenjie Wang Jingjing Huang Yao Li Haojie Ren Yuxuan Xiao Yuru Peng Jianmin Ji Yu Zhang Yanyong Zhang
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.