A model-based vehicle segmentation method for tracking
- 1 January 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1124-1131Vol.2
- https://doi.org/10.1109/iccv.2005.11
Abstract
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 modelsKeywords
This publication has 14 references indexed in Scilit:
- Rapid object detection using a boosted cascade of simple featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A Novel Method for Resolving Vehicle Occlusion in a Monocular Traffic-Image SequenceIEEE Transactions on Intelligent Transportation Systems, 2004
- Bayesian human segmentation in crowded situationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Robust tracking of humans and vehicles in cluttered scenes with occlusionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Detecting moving shadows: algorithms and evaluationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Occlusion robust tracking utilizing spatio-temporal Markov random field modelPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A statistical method for 3D object detection applied to faces and carsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Detection and classification of vehiclesIEEE Transactions on Intelligent Transportation Systems, 2002
- Higher order statistical learning for vehicle detection in imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Visual interpretation of known objects in constrained scenesPhilosophical Transactions Of The Royal Society B-Biological Sciences, 1992