Title :
On-road vehicle detection: a review
Author :
Sun, Zehang ; Bebis, George ; Miller, Ronald
Author_Institution :
ETreppid Technol., Reno, NV, USA
fDate :
5/1/2006 12:00:00 AM
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.
Keywords :
automobiles; driver information systems; object detection; optical sensors; road accidents; intelligent vehicle; on-board automotive driver assistance systems; on-road vehicle detection; optical sensors; temporal continuity; Accidents; Automotive engineering; Intelligent sensors; Intelligent vehicles; Mobile robots; Remotely operated vehicles; Road vehicles; Robustness; Vehicle detection; Vehicle driving; Vehicle detection; computer vision; intelligent vehicles.; Accidents, Traffic; Algorithms; Artificial Intelligence; Automobile Driving; Automobiles; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Safety;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2006.104