MIT DriveSeg (Manual) Dataset

Semantic scene segmentation has primarily been addressed by forming representations of single images both with supervised and unsupervised methods. The problem of semantic segmentation in dynamic scenes has begun to recently receive attention with video object segmentation approaches. What is not known is how much extra information the temporal dynamics of the visual scene carries that is complimentary to the information available in the individual frames of the video. We provide the MIT DriveSeg dataset, which is a large-scale driving scene segmentation dataset, densely annotated for every pixel and every one of 5,000 video frames. The purpose of this dataset is to allow for exploration of the value of temporal dynamics information for full scene segmentation in dynamic, real-world operating environments.