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.

Web-based mapping technologies have been utilized for traffic information systems. However, most such systems are for freeways and very few of them focus on arterials or urban streets. This paper presents a real-time Google-map-based Arterial Traffic Information (GATI) system for urban streets in the City of Bellevue, Washington State. Open source web tools and emerging web technologies, such as Ajax, are used in implementing the system to ensure its performance and minimize its cost. Convenient administrative functions are enabled through advanced database design and the Model-View-Controller (MVC) application. This GATI system, though presented and demonstrated by using Bellevue's data, is a general technology that can be applied to any arterial network.

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