Vehicle Segmentation and Classification Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generic deformable model for vehicle recognition
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
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
Vehicle Class Recognition from Video-Based on 3D Curve Probes
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
3-D model-based vehicle tracking
IEEE Transactions on Image Processing
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We present an approach to 3D vehicle class recognition (which of SUV, mini-van, sedan, pickup truck) with one or more fixed video-cameras in arbitrary positions with respect to a road. The vehicle motion is assumed to be straight. We propose an efficient method of Structure from Motion (SfM) for camera calibration and 3D reconstruction. 3D geometry such as vehicle and cabin length, width, height, and functions of these are computed and become features for use in a classifier. Classification is done by a minimum probability of error recognizer. Finally, when additional video clips taken elsewhere are available, we design classifiers based on two or more video clips, and this results in significant classification-error reduction.