  Traffic scene perception in computer vision is a critically important task to
achieve intelligent cities. To date, most existing datasets focus on autonomous
driving scenes. We observe that the models trained on those driving datasets
often yield unsatisfactory results on traffic monitoring scenes. However,
little effort has been put into improving the traffic monitoring scene
understanding, mainly due to the lack of specific datasets. To fill this gap,
we introduce a specialized traffic monitoring dataset, termed TSP6K, containing
images from the traffic monitoring scenario, with high-quality pixel-level and
instance-level annotations. The TSP6K dataset captures more crowded traffic
scenes with several times more traffic participants than the existing driving
scenes. We perform a detailed analysis of the dataset and comprehensively
evaluate previous popular scene parsing methods, instance segmentation methods
and unsupervised domain adaption methods. Furthermore, considering the vast
difference in instance sizes, we propose a detail refining decoder for scene
parsing, which recovers the details of different semantic regions in traffic
scenes owing to the proposed TSP6K dataset. Experiments show its effectiveness
in parsing the traffic monitoring scenes. Code and dataset are available at
https://github.com/PengtaoJiang/TSP6K.
