Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and traditional blob tracking methods is unable to separate the vehicles in the merged blobs. We use vehicle shape models, in addition to camera calibration and ground plane knowledge, to detect, track and classify moving vehicles in presence of occlusion. We use a 2-stage approach. In the first stage, hypothesis for vehicle types, positions and orientations are formed by a coarse search, which is then refined by a data driven Markov chain Monte Carlo (DDMCMC) process. We show results and evaluations on some real urban traffic video sequence using three types of vehicle models.
A model-based vehicle segmentation method for tracking
Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 ; 2 ; 1124-1131 Vol. 2
2005-01-01
385749 byte
Conference paper
Electronic Resource
English
A Model-Based Vehicle Segmentation Method for Tracking
British Library Conference Proceedings | 2005
|Vehicle Segmentation and Tracking in the Presence of Occlusions
Transportation Research Record | 2006
|Vehicle Segmentation and Tracking in the Presence of Occlusions
British Library Conference Proceedings | 2006
|Vehicle Segmentation and Tracking in the Presence of Occlusions
Online Contents | 2006
|Vehicle Segmentation and Tracking in the Presence of Occlusions
Transportation Research Record | 2006
|