Estimating the driving state of oncoming vehicles from a moving platform using stereo vision
IEEE Transactions on Intelligent Transportation Systems
Feature clustering for vehicle detection and tracking in road traffic surveillance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
Incremental unsupervised three-dimensional vehicle model learning from video
IEEE Transactions on Intelligent Transportation Systems
A taxonomy and analysis of camera calibration methods for traffic monitoring applications
IEEE Transactions on Intelligent Transportation Systems
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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We present a method for segmenting and tracking vehicles on highways using a camera that is relatively low to the ground. At such low angles, 3-D perspective effects cause significant changes in appearance over time, as well as severe occlusions by vehicles in neighboring lanes. Traditional approaches to occlusion reasoning assume that the vehicles initially appear well separated in the image; however, in our sequences, it is not uncommon for vehicles to enter the scene partially occluded and remain so throughout. By utilizing a 3-D perspective mapping from the scene to the image, along with a plumb line projection, we are able to distinguish a subset of features whose 3-D coordinates can be accurately estimated. These features are then grouped to yield the number and locations of the vehicles, and standard feature tracking is used to maintain the locations of the vehicles over time. Additional features are then assigned to these groups and used to classify vehicles as cars or trucks. Our technique uses a single grayscale camera beside the road, incrementally processes image frames, works in real time, and produces vehicle counts with over 90% accuracy on challenging sequences.