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End-to-End Pedestrian Collision Warning System Based on a Convolutional Neural Network with Semantic Segmentation
- End-to-End Pedestrian Collision Warning System Based on a Convolutional Neural Network with Semantic Segmentation
- Jung, Heechul; Choi, Min-Kook; Kwon, Soon; Jung, Woo Young
- DGIST Authors
- Choi, Min-Kook; Kwon, Soon; Jung, Woo Young
- Issue Date
- 2018 IEEE International Conference on Consumer Electronics, ICCE 2018, 1-3
- 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.
- Institute of Electrical and Electronics Engineers Inc.
- Related Researcher
Artificial Intelligence, Machine Learning, Autonomous Driving
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- Convergence Research Center for Future Automotive Technology2. Conference Papers
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