Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A Compact Vision System for Road Vehicle Guidance
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Nighttime Vehicle Detection for Intelligent Headlight Control
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Vision-based vehicle detection for a driver assistance system
Computers & Mathematics with Applications
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Being aware of other vehicles on the road ahead is a key information to help driver assistance systems to increase driver's safety. This paper addresses this problem, proposing a system to detect vehicles from the images provided by a single camera mounted in a mobile platform. A classifier-based approach is presented, based on the evaluation of a cascade of classifiers (COC) at different scanned image regions. The Adaboost algorithm is used to determine the COC from training sets. Two proposals are done to reduce the computation needed for the detection scheme used: a lazy evaluation of the COC, and the customization of the COC by a wrapping process. The benefits of these two proposals are quantified in terms of the average number of image features required to classify an image region, achieving a reduction of the 58% on this concept, while scarcely penalizing the detection accuracy of the system.