Machine Learning
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
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
An Oriented-Contour Point Based Voting Algorithm for Vehicle Type Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
Vehicle logo recognition using a SIFT-based enhanced matching scheme
IEEE Transactions on Intelligent Transportation Systems
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Neural network approach to identify model of vehicles
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Classification of vehicle type and make by combined features and random subspace ensemble
International Journal of Computational Vision and Robotics
Combination of Feature Extraction Methods for SVM Pedestrian Detection
IEEE Transactions on Intelligent Transportation Systems
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Vehicle logo detection from images captured by surveillance cameras is an important step towards the vehicle recognition that is required for many applications in intelligent transportation systems and automatic surveillance. The task is challenging considering the small target of logos and the wide range of variability in shape, color, and illumination. A fast and reliable vehicle logo detection approach is proposed following visual attention mechanism from the human vision. Two prelogo detection steps, that is, vehicle region detection and a small RoI segmentation, rapidly focalize a small logo target. An enhanced Adaboost algorithm, together with two types of features of Haar and HOG, is proposed to detect vehicles. An RoI that covers logos is segmented based on our prior knowledge about the logos' position relative to license plates, which can be accurately localized from frontal vehicle images. A two-stage cascade classier proceeds with the segmented RoI, using a hybrid of Gentle Adaboost and Support Vector Machine (SVM), resulting in precise logo positioning. Extensive experiments were conducted to verify the efficiency of the proposed scheme.