MSSP: A Multi-view Benchmark for Street Scene Perception in Assistive Navigation

Finding safe paths in autonomous or assistive navigation systems is a challenging task. In this paper, we introduce MSSP, a novel multi-perspective street scene perception benchmark dataset for navigation assistance. Compared to single-view perception, multi-view provides more comprehensive information about the surroundings to the pedestrians for enhancing safety. The MSSP dataset includes 2,044 samples from complex scenes in both first-person and third-person perspectives, covering four categories: Sidewalk, Traffic lane, Verge, and Lawn. This dataset primarily focuses on pedestrian navigation assistance and is the first pedestrian multi-perspective dataset. Furthermore, in comparison to single-perspective datasets in other studies, MSSP contains more categories. The dataset supports pixel-level semantic segmentation approaches. We also provide experimental results of recent semantic segmentation methods on this dataset for evaluation. Specifically, the transformer-based SegFormer method outperforms others on MSSP. Our dataset is accessible through: https://github.com/yangdi-cv/MSSP.