MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
One-class svms for document classification
The Journal of Machine Learning Research
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing in Java
Digital Image Processing in Java
On-road vehicle detection using evolutionary Gabor filter optimization
IEEE Transactions on Intelligent Transportation Systems
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In this paper, we present a real-time forward vehicle detection warning system using a novel image representation called an equi-height mosaicking image. The proposed system uses a GPU (graphic processing unit) based approach for the real-time processing of a road scene image captured from a single camera. The equi-height mosaicking image improves the execution time of the existing GPU-based acceleration approach without decreasing the detection accuracy. The equi-height image is generated as follows. After a geometric analysis of a road scene using the vanishing point and horizon, we crop a set of image strips by sampling several positions on the road at uniform intervals. The height of each image strip is computed by projecting the predefined height of a vehicle at a distant position onto an image plane. After all the cropped images are resized to the uniform height required to build the equi-height image, we concatenate these resized images, similar to a panorama image, to create the equi-height mosaicking image. The concatenated image has a long width but the height of the image is uniform. The proposed system then performs a GPU-based vehicle detection on the concatenated image using a 1D search based support vector machine (SVM) classification. The proposed method is faster than the GPU-based OpenCV HOG detector because of the reduced search area.