01128605

Component

https://doi.org/10.3141/2121-09

http://scholar.google.com...Wang&publication_year=2009

Surveillance video cameras have been increasingly deployed along roadways over the past decade. Automatic traffic data collection through surveillance video cameras is highly desirable; however, sight-degrading factors and camera vibrations make it an extremely challenging task. In this paper, a computer-vision–based algorithm for vehicle detection and tracking is presented, implemented, and tested. This new algorithm consists of four steps: user initialization, spatiotemporal map generation, strand analysis, and vehicle tracking. It relies on a single, environment-insensitive cue that can be easily obtained and analyzed without camera calibration. The proposed algorithm was implemented in Microsoft Visual C++ using OpenCV and Boost C++ graph libraries. Six test video data sets, representing a variety of lighting, flow level, and camera vibration conditions, were used to evaluate the performance of the new algorithm. Experimental results showed that environmental factors do not significantly impact the detection accuracy of the algorithm. Vehicle count errors ranged from 8% to 19% in the tests, with an overall average detection accuracy of 86.6%. Considering that the test scenarios were chosen to be challenging, such test results are encouraging.

01147424

09-1580

English

0000-0002-4180-5628

pp 81-89

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309126380

Print

Figures (9) ; References (23) ; Tables (1)

Accuracy; Algorithms; Automatic data collection systems; Computer vision; Traffic data; Traffic surveillance; Vibration; Video cameras

Spatiotemporal traffic contour maps; Vehicle detection and tracking; Vehicle tracking

Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control

TRIS, TRB, ATRI

Jan 30 2009 5:49PM

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