Real-Time Instance Segmentation of Pedestrians using Transfer Learning

Real-time instance segmentation of pedestrians presents a critical core task within an automated driving pipeline. Recent research focuses on existing real-world datasets to train their instance segmentation networks. However, due to the limited size of real-world datasets, they tend to either overfit or lack accuracy. Therefore, these networks remain useless for real-world applications. Hence, we introduce a transfer learning strategy by combining a large-scale synthetic dataset and a real-world dataset for instance segmentation of pedestrians. We showcase our approach on three state-of-the-art real-time instance segmentation methods: (1) YOLACT++, (2) SipMask, and (3) BlendMask. Finally, we provide a quantitative and qualitative evaluation of our introduced approach on two publicly available urban street scenes datasets, i.e. the real-world Cityscapes dataset and the synthetic Synscapes dataset.