Image-Based Learning to Measure Traffic Density Using a Deep Convolutional Neural Network
- Authors
- Chung, Jiyong; Sohn, Keemin
- Issue Date
- May-2018
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Deep convolutional neural network (CNN); machine learning; traffic density; vehicle counting
- Citation
- IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.19, no.5, pp 1670 - 1675
- Pages
- 6
- Journal Title
- IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Volume
- 19
- Number
- 5
- Start Page
- 1670
- End Page
- 1675
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/2213
- DOI
- 10.1109/TITS.2017.2732029
- ISSN
- 1524-9050
1558-0016
- Abstract
- Existing methodologies to count vehicles from a road image have depended upon both hand-crafted feature engineering and rule-based algorithms. These require many predefined thresholds to detect and track vehicles. This paper provides a supervised learning methodology that requires no such feature engineering. A deep convolutional neural network was devised to count the number of vehicles on a road segment based solely on video images. The present methodology does not regard an individual vehicle as an object to be detected separately; rather, it collectively counts the number of vehicles as a human would. The test results show that the proposed methodology outperforms existing schemes.
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