NDLI: A New Vehicle Detection Approach in Traffic Jam Conditions

Content Provider	IEEE Xplore Digital Library
Author	Bing-Fei Wu Jhy-Hong Juang Ping-Tsung Tsai Ming-Wei Chang Chung-Jui Fan Shin-Ping Lin Wu, J.Y.-J. Hsia Lee
Copyright Year	2007
Description	Author affiliation: Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu (Bing-Fei Wu; Jhy-Hong Juang; Ping-Tsung Tsai; Ming-Wei Chang; Chung-Jui Fan; Shin-Ping Lin; Wu, J.Y.-J.; Hsia Lee)
Abstract	The well-known vehicle detectors utilize the background extraction methods to segment the moving objects. The background updating concept is applied to overcome the luminance variation which results in the error detection. These systems will meet a challenge when detecting the vehicles in the traffic jam conditions at sunset. The vehicles will cover the road surface so that the background information cannot be smoothly updated. Once the traffic is released, the existing background is not suitable for the moving segmentation. The main contribution of this paper is that an efficient vehicle detection approach is proposed to improve the detection accuracy in traffic jam conditions. The land mask decision gives the land information and the merged boundary box rule is presented to realize vehicle detection. The signed square normalized correlation coefficient calculation is addressed, and it is applied to vehicle tracking. The experimental results show that this approach works well in highway and urban area with high accuracy
Starting Page	1
Ending Page	6
File Size	524144
Page Count	6
File Format	PDF
ISBN	1424407079
DOI	10.1109/CIISP.2007.369284
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Infrared detectors Vehicle Detection Automated highways Vehicle dynamics tracking Control engineering Intelligent vehicles Vehicle detection traffic jam Road vehicles Object detection Signal processing Computational intelligence
Content Type	Text
Resource Type	Article

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