A video-based real-time vehicle detection method by classified background learning

Tan, Xiao-jun, Li, Jun and Liu, Chunlu 2007, A video-based real-time vehicle detection method by classified background learning, World transactions on engineering and technology education, vol. 6, no. 1, pp. 189-192.


Title A video-based real-time vehicle detection method by classified background learning
Author(s) Tan, Xiao-jun
Li, Jun
Liu, Chunlu
Journal name World transactions on engineering and technology education
Volume number 6
Issue number 1
Start page 189
End page 192
Publisher UNESCO, International Centre for Engineering Education (UICEE)
Place of publication Clayton, Vic.
Publication date 2007
ISSN 1446-2257
Summary A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using
line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.
Language eng
Field of Research 090507 Transport Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, UICEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007392

Document type: Journal Article
Collection: School of Architecture and Built Environment
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