A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Spectrum - Critical challenges 2002
A Real-time Precrash Vehicle Detection System
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Vision-based control of uncertain systems
Vision-based control of uncertain systems
Robust Real-Time Face Detection
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-Time Vehicle Detection and Tracking System in Outdoor Traffic Scenes
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Hybrid Force and Vision-Based Contour Following of Planar Robots
Journal of Intelligent and Robotic Systems
On-line Conservative Learning for Person Detection
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Precise Positioning of Binocular Eye-to-Hand Robotic Manipulators
Journal of Intelligent and Robotic Systems
Multi-class Boosting with Color-Based Haar-Like Features
SITIS '07 Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System
Adaboost with totally corrective updates for fast face detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
S-adaboost and pattern detection in complex environment
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Visual perception of obstacles and vehicles for platooning
IEEE Transactions on Intelligent Transportation Systems
Preceding vehicle recognition based on learning from sample images
IEEE Transactions on Intelligent Transportation Systems
Stochastic car tracking with line- and color-based features
IEEE Transactions on Intelligent Transportation Systems
Neuroadaptive Combined Lateral and Longitudinal Control of Highway Vehicles Using RBF Networks
IEEE Transactions on Intelligent Transportation Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
Monocular precrash vehicle detection: features and classifiers
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Modular interpretation of low altitude aerial images of non-urban environment
Digital Signal Processing
| Hi-index | 0.00 |
This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and upto-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.