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Proceedings Paper

Determination of vehicle density from traffic images at day and nighttime
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Paper Abstract

In this paper we extend our previous work to address vehicle differentiation in traffic density computations1. The main goal of this work is to create vehicle density history for given roads under different weather or light conditions and at different times of the day. Vehicle differentiation is important to account for connected or otherwise long vehicles, such as trucks or tankers, which lead to over-counting with the original algorithm. Average vehicle size in pixels, given the magnification within the field of view for a particular camera, is used to separate regular cars and long vehicles. A separate algorithm and procedure have been developed to determine traffic density after dark when the vehicle headlights are turned on. Nighttime vehicle recognition utilizes blob analysis based on head/taillight images. The high intensity of vehicle lights are identified in binary images for nighttime vehicle detection. The stationary traffic image frames are downloaded from the internet as they are updated. The procedures are implemented in MATLAB. The results of both nighttime traffic density and daytime long vehicle identification algorithms are described in this paper. The determination of nighttime traffic density, and identification of long vehicles at daytime are improvements over the original work1.

Paper Details

Date Published: 26 February 2007
PDF: 12 pages
Proc. SPIE 6496, Real-Time Image Processing 2007, 649603 (26 February 2007); doi: 10.1117/12.700982
Show Author Affiliations
Mehrübe Mehrübeoğlu, Texas A&M Univ., Corpus Christi (United States)
Lifford McLauchlan, Texas A&M Univ., Kingsville (United States)


Published in SPIE Proceedings Vol. 6496:
Real-Time Image Processing 2007
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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