This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done, at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the user.
|Original language||English (US)|
|Number of pages||11|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|State||Published - Mar 2002|
Bibliographical noteFunding Information:
Dr. Masoud is the recipient of a Research Contribution Award from the University of Minnesota, the Rosemount Instrumentation Award from Rosemount Inc., Chanhassen, MN, and the Matt Huber Award for Excellence in Transportation Research.
Manuscript received April 2001; revised February 25, 2002. This work was supported in part by the ITS Institute at the University of Minnesota, by the Minnesota Department of Transportation, and in part by the National Science Foundation under Grant CMS-0127893. The Guest Editor for this paper was P. A. Ioannou.
- Camera calibration
- Vehicle classification
- Vehicle detection
- Vehicle tracking