Machine Learning
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
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
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Integrated Computer-Aided Engineering
Flexible background mixture models for foreground segmentation
Image and Vision Computing
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Automatic image search based on improved feature descriptors and decision tree
Integrated Computer-Aided Engineering
Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles
Integrated Computer-Aided Engineering
A Multiple-Hypothesis Approach for Multiobject Visual Tracking
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Hierarchical Method for Foreground Detection Using Codebook Model
IEEE Transactions on Circuits and Systems for Video Technology
Journal of Mathematical Imaging and Vision
Integration of emerging computer technologies for an efficient image sequences analysis
Integrated Computer-Aided Engineering
Human automatic detection and tracking for outdoor video
Integrated Computer-Aided Engineering
Improving fusion with optimal weight selection in Face Recognition
Integrated Computer-Aided Engineering
Reconstruction of occluded facial images using asymmetrical Principal Component Analysis
Integrated Computer-Aided Engineering
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A Support Vector Machine SVM is an effective method for pedestrian detection applications; however, performance of an SVM is closely related to the samples that are used to train it. An SVM classifier trained by samples from well-known pedestrian datasets such as INRIA and MIT is observed to have limited detection capability in practical environments. In this paper, a statistical background-foreground extraction approach is proposed that autonomously generates samples containing pedestrians in real scenes, in order to diversify the basic training set of the SVM. Comparative experiments have shown that the SVM classifier's discriminability and adaptability to a new scene are greatly enhanced by utilizing extracted samples from that scene in the training stage. Here, a pedestrian tracker that combines a Camshift tracker and a Kalman filter is adjoined to the pedestrian classifier; the tracker is proved to be robust against pose and scale changes, abrupt direction of motion changes, and occlusions, in several test scenes.