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
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust Real-Time Face Detection
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
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised outlier detection based on fuzzy rough C-means clustering
Mathematics and Computers in Simulation
An embedded system for real-time facial expression recognition based on the extension theory
Computers & Mathematics with Applications
Vision-based vehicle detection for a driver assistance system
Computers & Mathematics with Applications
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Computers & Mathematics with Applications
Gabor-Based Region Covariance Matrices for Face Recognition
IEEE Transactions on Circuits and Systems for Video Technology
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
IEEE Transactions on Circuits and Systems for Video Technology
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This work presents a novel pedestrian detection system that uses Haar-like feature extraction and a covariance matrix descriptor to identify the distinctions of pedestrians. An approach that adopts an integral image is also applied to reduce the computational loads required in both the Haar-like feature extraction and evaluation of the covariance matrix descriptor. Based on the Fisher linear discriminant analysis (FLDA) classification algorithm, the proposed system can classify pedestrians efficiently. Additionally, the detection procedure of the proposed system is accelerated using a two-layer cascade of classifiers. The front end, constructed based on Haar-like features, can select candidate regions quickly wherever pedestrians may be present. Moreover, the back end, constructed based on the covariance matrix descriptor, can determine accurately whether pedestrians are positioned in candidate regions. If a region tests positive through the two-layer cascade classifiers, pedestrian images are likely captured. Test video sequences during the experiments are taken from a test set of the INRIA person database, using 30 input frames per second, each with a resolution of 320x240 pixels. Experimental results demonstrate that the proposed system can detect pedestrians efficiently and accurately, significantly contributing to efforts to develop a real time system.