Moving Object Segmentation (MOS), a crucial task in computer vision, has numerous applications such as surveillance, autonomous driving, and video analytics. Existing datasets for moving object segmentation mainly focus on RGB or Lidar videos, but lack additional event information that can enhance the understanding of dynamic scenes. To address this limitation, we propose a novel dataset, called DSEC-MOS. Our dataset includes frames captured by RGB cameras embedded on moving vehicules and incorporates event data, which provide high temporal resolution and low-latency information about changes in the scenes. To generate accurate segmentation mask annotations for moving objects, we apply the recently emerged large model SAM - Segment Anything Model - with moving object bounding boxes from DSEC-MOD serving as prompts and calibrated RGB frames, then further revise the results. Our DSEC-MOS dataset contains in total 16 sequences (13314 images). To the best of our knowledge, DSEC-MOS is also the first moving object segmentation dataset that includes event camera in autonomous driving. Project Page: https://github.com/ZZY-Zhou/DSEC-MOS.

本研究提出了一种新的数据集DSEC-MOS，用于解决现有运动目标分割数据集中缺乏提高动态场景理解的事件信息的问题，该数据集包含16个序列和13314张图像，并利用大型模型SAM和事件数据生成准确的分割掩码注释。

DSEC-MOS:使用运动自车分割任何运动物体