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Title:

Real-Time Instance Segmentation of Pedestrians using Transfer Learning*

Document type:
Konferenzbeitrag
Author(s):
Žagar, Bare Luka; Preintner, Tobias; Knoll, Alois C.; Yurtsever, Ekim
Abstract:
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 re...     »
Horizon 2020:
This result is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870133.
Book / Congress title:
2022 27th International Conference on Automation and Computing (ICAC)
Year:
2022
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