ShadowCam: Real-Time Detection Of Moving Obstacles Behind A Corner For Autonomous Vehicles
Moving obstacles occluded by corners are a potential source for collisions in mobile robotics applications such as autonomous vehicles. In this paper, we address the problem of anticipating such collisions by proposing a vision-based detection algorithm for obstacles which are outside of a vehicle’s...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
IEEE
2018
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| Online Access: | http://hdl.handle.net/1721.1/119439 https://orcid.org/0000-0003-2752-2311 https://orcid.org/0000-0002-9673-1267 https://orcid.org/0000-0001-9919-069X https://orcid.org/0000-0003-4915-0256 https://orcid.org/0000-0001-9166-4758 https://orcid.org/0000-0002-2231-7995 https://orcid.org/0000-0001-5473-3566 |
| Summary: | Moving obstacles occluded by corners are a potential source for collisions in mobile robotics applications such as autonomous vehicles. In this paper, we address the problem of anticipating such collisions by proposing a vision-based detection algorithm for obstacles which are outside of a vehicle’s
direct line of sight. Our method detects shadows of obstacles hidden around corners and automatically classifies these unseen obstacles as “dynamic” or “static”. We evaluate our proposed detection algorithm on real-world corners and a large variety of simulated environments to assess generalizability in different challenging surface and lighting conditions. The mean classification
accuracy on simulated data is around 80% and on realworld corners approximately 70%. Additionally, we integrate our detection system on a full-scale autonomous wheelchair and demonstrate its feasibility as an additional safety mechanism through real-world experiments. We release our real-timecapable implementation of the proposed ShadowCam algorithm
and the dataset containing simulated and real-world data under an open-source license |
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