Using A Priori Knowledge to Improve Scene Understanding

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as autonomous driving. Existing algorithms treat each image in isolation, but autonomous vehicles often revisit the same locations. We propose leveraging this a priori knowledge to improve semantic segmentation of images from sequential driving datasets. We examine several methods to fuse these temporal scene priors, and introduce a prior fusion network that is able to learn how to transfer this information. Our model improves the accuracy of dynamic object classes from 69.1% to 73.3%, and static classes from 88.2% to 89.1%.


Published in:
2019 Ieee/Cvf Conference On Computer Vision And Pattern Recognition Workshops (Cvprw 2019), 487-489
Presented at:
32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, Jun 16-20, 2019
Year:
Jan 01 2019
Publisher:
New York, IEEE
ISSN:
2160-7508
ISBN:
978-1-7281-2506-0




 Record created 2020-10-02, last modified 2020-10-29


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