Abstract
Traffic scene parsing is one of the most important tasks to achieve intelligent cities. So far, little effort has been spent on constructing datasets specifically for the task of traffic scene parsing. To fill this gap, here we introduce the TSP6K dataset, containing 6,000 urban traffic images and spanning hundreds of street scenes under various weather conditions. In contrast to most previous traffic scene datasets collected from a driving platform, the images in our dataset are from the shooting platform high-hanging on the street. Such traffic images can capture more crowded street scenes with several times more traffic participants than the driving scenes. Each image in the TSP6K dataset is provided with high-quality pixel-level and instance-level annotations. We perform a detailed analysis for the dataset and comprehensively evaluate the state-of-the-art scene parsing methods. Considering the vast difference in instance sizes, we propose a detail refining decoder, which recovers the details of different semantic regions in traffic scenes. Experiments have shown its effectiveness in parsing high-hanging traffic scenes. Code and dataset will be made publicly available.
Abstract (translated)
交通场景解析是实现智慧城市的最重要任务之一。迄今为止, little effort has been spent on constructing datasets specifically for the task of traffic scene parsing. 为填补这一差距,我们介绍了TSP6K dataset,其中包括6,000幅城市交通图像,涵盖数百个街道场景,在各种天气条件下。与大多数从驾驶平台收集的交通场景数据集不同,我们的数据集中的图像是从街道的高架上拍摄的。这些交通图像能够捕捉到比驾驶场景更加拥挤的街道场景,并有数倍于驾驶场景的交通参与者。每个图像在TSP6K dataset中都有高质量的像素级和实例级注释。我们对dataset进行了详细的分析,并全面评估了交通场景解析的最新方法。考虑到实例大小的巨大差异,我们提出了一种细节 refine Decoder,该算法可以恢复交通场景不同语义区域的详细信息。实验表明,它在解析高架上的交通场景方面非常有效。代码和数据集将公开可用。
URL
https://arxiv.org/abs/2303.02835
