TY - GEN
T1 - Real-Time Instance Segmentation of Pedestrians using Transfer Learning
AU - Zagar, Bare Luka
AU - Preintner, Tobias
AU - Knoll, Alois C.
AU - Yurtsever, Ekim
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Automated Driving Systems
KW - Real-Time Instance Segmentation
KW - Synthetic Data
KW - Transfer Learning
UR - http://www.scopus.com/inward/record.url?scp=85141153726&partnerID=8YFLogxK
U2 - 10.1109/ICAC55051.2022.9911121
DO - 10.1109/ICAC55051.2022.9911121
M3 - Conference contribution
AN - SCOPUS:85141153726
T3 - 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022
BT - 2022 27th International Conference on Automation and Computing
A2 - Yang, Chenguang
A2 - Xu, Yuchun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Automation and Computing, ICAC 2022
Y2 - 1 September 2022 through 3 September 2022
ER -