Sun Zehang, Bebis George, Miller Ronald
eTreppid Technologies LLC, Reno, NV 89521, USA.
IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):694-711. doi: 10.1109/TPAMI.2006.104.
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.
开发旨在向驾驶员警示驾驶环境以及与其他车辆可能发生碰撞的车载汽车驾驶辅助系统，近来已引起了广泛关注。在这些系统中，强大且可靠的车辆检测是关键一步。本文对近期基于视觉的道路车辆检测系统进行了综述。我们关注的是摄像头安装在车辆上而非像交通/车道监测系统那样固定不动的系统。首先，我们讨论使用光学传感器进行道路车辆检测的问题，随后简要回顾全球范围内的智能车辆研究。然后，我们讨论有源和无源传感器，为基于视觉的车辆检测奠定基础。接下来将综述旨在快速推测图像中车辆位置以及验证推测位置的方法。还将综述检测与跟踪的整合，以说明利用时间连续性进行车辆检测的益处。最后，我们对所讨论的方法进行批判性概述，评估它们未来部署的潜力，并给出未来研究的方向。
