A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based object tracking in monocular image sequences of road traffic scenes
International Journal of Computer Vision
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Model-based vision for traffic scenes using the ground-plane constraint
Real-time computer vision
International Journal of Computer Vision
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-time Computer Vision System for Measuring Traffic Parameters
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Occlusion Robust Tracking Utilizing Spatio-Temporal Markov Random Field Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Automatic Eyeglasses Removal from Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Model-Based Vehicle Segmentation Method for Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Model-Based Approach for Estimating Human 3D Poses in Static Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Augmenting Shape with Appearance in Vehicle Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Sequential Vehicle Classifier for Infrared Video using Multinomial Pattern Matching
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
A novel method for resolving vehicle occlusion in a monocular traffic-image sequence
IEEE Transactions on Intelligent Transportation Systems
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In this paper, we propose a new vehicle detection approach based on Markov chain Monte Carlo (MCMC). We mainly discuss the detection of vehicles in front-view static images with frequent occlusions. Models of roads and vehicles based on edge information are presented, the Bayesian problem's formulations are constructed, and a Markov chain is designed to sample proposals to detect vehicles. Using the Monte Carlo technique, we detect vehicles sequentially based on the idea of maximizing a posterior probability (MAP), performing vehicle segmentation in the meantime. Our method does not require complex preprocessing steps such as background extraction or shadow elimination, which are required in many existing methods. Experimental results show that the method has a high detection rate on vehicles and can perform successful segmentation, and reduce the influence caused by vehicle occlusion.