An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Face Detection Based on Cost-Sensitive Support Vector Machines
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Robust Real-Time Face Detection
International Journal of Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
A New Multiple Classifiers Combination Algorithm
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) - Volume 02
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
Object detection using image reconstruction with PCA
Image and Vision Computing
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
GVE '07 Proceedings of the IASTED International Conference on Graphics and Visualization in Engineering
The treelike assembly classifier for pedestrian detection
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Cascaded SVMs in Pattern Classification for Time-Sensitive Separating
IITSI '10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining histograms of oriented gradients with global feature for human detection
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Nighttime pedestrian detection with a normal camera using SVM classifier
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
A Low-Cost Pedestrian-Detection System With a Single Optical Camera
IEEE Transactions on Intelligent Transportation Systems
Reducing SVM classification time using multiple mirror classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
A novel multiplex cascade classifier for pedestrian detection
Pattern Recognition Letters
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In this paper, we propose a cascade classifier combining AdaBoost and support vector machine, and applied this to pedestrian detection. The pedestrian detection involved using a window of fixed size to extract the candidate region from left to right and top to bottom of the image, and performing feature extractions on the candidate region. Finally, our proposed cascade classifier completed the classification of the candidate region. The cascade-AdaBoost classifier has been successfully used in pedestrian detection. We have improved the initial setting method for the weights of the training samples in the AdaBoost classifier, so that the selected weak classifier would be able to focus on a higher detection rate other than accuracy. The proposed cascade classifier can automatically select the AdaBoost classifier or SVM to construct a cascade classifier according to the training samples, so as to effectively improve classification performance and reduce training time. In order to verify our proposed method, we have used our extracted database of pedestrian training samples, PETs database, INRIA database and MIT database. This completed the pedestrian detection experiment whose result was compared to those of the cascade-AdaBoost classifier and support vector machine. The result of the experiment showed that in a simple environment involving campus experimental image and PETs database, both our cascade classifier and other classifiers can attain good results, while in a complicated environment involving INRA and MIT database experiments, our cascade classifier had better results than those of other classifiers.