Deep Open Space Segmentation using Automotive Radar
- URL: http://arxiv.org/abs/2004.03449v1
- Date: Wed, 18 Mar 2020 14:49:29 GMT
- Title: Deep Open Space Segmentation using Automotive Radar
- Authors: Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor, Fahed Al
Hassanat, Elnaz Jahani Heravi, Robert Laganiere, Julien Rebut, Waqas Malik
- Abstract summary: We propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios.
Our proposed approach achieves low memory usage and real-time processing speeds, and is thus very well suited for embedded deployment.
- Score: 3.3714322233611997
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we propose the use of radar with advanced deep segmentation
models to identify open space in parking scenarios. A publically available
dataset of radar observations called SCORP was collected. Deep models are
evaluated with various radar input representations. Our proposed approach
achieves low memory usage and real-time processing speeds, and is thus very
well suited for embedded deployment.
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