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      On-road vehicle detection: a review.

      IEEE transactions on pattern analysis and machine intelligence

      Accidents, Traffic, prevention & control, Algorithms, Artificial Intelligence, Automobile Driving, Automobiles, Image Interpretation, Computer-Assisted, methods, Pattern Recognition, Automated, Safety

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          Abstract

          Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

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          Journal
          16640257
          10.1109/TPAMI.2006.104

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