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Methodology for Improving Detection Speed of Pedestrians in Autonomous Vehicle by Image Class Classification

Title
Methodology for Improving Detection Speed of Pedestrians in Autonomous Vehicle by Image Class Classification
Authors
Kim, JunkwangJung, Woo YoungJung, HeechulHan, Dong Seog
DGIST Authors
Kim, Junkwang; Jung, Woo Young
Issue Date
2018-01-14
Citation
2018 IEEE International Conference on Consumer Electronics, ICCE 2018, 1-2
Type
Conference
ISBN
9781538630259
Abstract
We propose a pedestrian detection method to minimize the amount of computation for classifying and candidate region detection in autonomous vehicles. The minimization of the computational complexity is a crucial factor for commercial products with a limited computational power. In conventional pedestrian detection methods, the number of candidate regions is 300 to 2,000 even if there is no pedestrian in an image. Therefore, the unnecessary computation is significant to classify each falsely decided candidate region. Moreover, it leads to false detection. In this paper, we propose a new methodology for solving this problem, and show through experiments that the processing speed can be improved by the proposed methodology. © 2018 IEEE.
URI
http://hdl.handle.net/20.500.11750/8992
DOI
10.1109/ICCE.2018.8326252
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:
Division of Automotive Technology2. Conference Papers


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