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End-to-End Pedestrian Collision Warning System Based on a Convolutional Neural Network with Semantic Segmentation

Title
End-to-End Pedestrian Collision Warning System Based on a Convolutional Neural Network with Semantic Segmentation
Authors
Jung, HeechulChoi, Min-KookKwon, SoonJung, Woo Young
DGIST Authors
Choi, Min-Kook; Kwon, SoonJung, Woo Young
Issue Date
2018-01-14
Citation
2018 IEEE International Conference on Consumer Electronics, ICCE 2018, 1-3
Type
Conference
ISBN
9781538630259
Abstract
Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e.g., when all pedestrians are walking on the sidewalk). These false alarms can make it difficult for drivers to concentrate on their driving. In this paper, we propose a novel framework for an end-to-end pedestrian collision warning system based on a convolutional neural network. Semantic segmentation information is used to train the convolutional neural network and two loss functions, such as cross entropy and Euclidean losses, are minimized. Finally, we demonstrate the effectiveness of our method in reducing false alarms and increasing warning accuracy compared to a traditional histogram of oriented gradients (HOG)-based system. © 2018 IEEE.
URI
http://hdl.handle.net/20.500.11750/8993
DOI
10.1109/ICCE.2018.8326129
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Jung, Wooyoung  
  • Research Interests Artificial Intelligence, Machine Learning, Autonomous Driving
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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