Robust Parking Occupancy Monitoring System Using Random Forests
- Authors
- Cho, Woon; Park, Seokmok; Kim, Min-jae; Han, Sangpil; Kim, Minseo; Kim, Taewoo; Kim, Jaewoong; Paik, Joonki
- Issue Date
- Jan-2018
- Publisher
- IEEE
- Keywords
- Parking occupancy monitoring system; indoor parking lot; machine learning; part-based model
- Citation
- 2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC), v.2018-January, pp 359 - 362
- Pages
- 4
- Journal Title
- 2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC)
- Volume
- 2018-January
- Start Page
- 359
- End Page
- 362
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/56206
- DOI
- 10.23919/ELINFOCOM.2018.8330608
- ISSN
- 2377-8431
- Abstract
- In recent years, with the growing number of vehicles, the efficient parking management system has become necessary for large buildings. This paper presents a parking occupancy monitoring system which can automatically decide whether a vehicle is parked or empty in each parking space. There are many obstacles in the parking area, such as high diversity in car models, occlusion by other car, moving person, waste trash, and camera lens distortion, making it difficult to detect a vehicle. In order to solve all these problems, this paper proposes to use a part-based and machine learning-based vehicle detection algorithm. We demonstrate that the proposed method performs well on large indoor parking lot dataset which contains the abovementioned obstacles.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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