The goal of this project is to monitor activities at traffic intersections for detecting/predicting situations that may lead to accidents. Some of the key elements for robust intersection monitoring are camera calibration, motion tracking, incident detection, etc. In this paper, we consider the motion-tracking problem. A multilevel tracking approach using Kalman filter is presented for tracking vehicles and pedestrians at intersections. The approach combines low-level image-based blob tracking with high-level Kalman filtering for position and shape estimation. An intermediate occlusion-reasoning module serves the purpose of detecting occlusions and filtering relevant measurements. Motion segmentation is performed by using a mixture of Gaussian models which helps us achieve fairly reliable tracking in a variety of complex outdoor scenes. A visualization module is also presented. This module is very useful for visualizing the results of the tracker and serves as a platform for the incident detection module.
|Original language||English (US)|
|Number of pages||12|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|State||Published - Jun 2003|
Bibliographical noteFunding Information:
Manuscript received December 16, 2002; revised September 15, 2003. This work was supported by the ITS Institute at the University of Minnesota, the Minnesota Department of Transportation, and the National Science Foundation under Grants CMS-0127893 and IIS-0219863. The Guest Editors for this paper were R. L. Cheu, D. Srinivasan, and D.-H. Lee.