Tracking and data association
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4S: A real-time system detecting and tracking people in 2 1/2D
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Unified Framework for Regularization Networks and Support Vector Machines
A Unified Framework for Regularization Networks and Support Vector Machines
The KidsRoom: A Perceptually-Based Interactive and Immersive Story Environment
Presence: Teleoperators and Virtual Environments
Regularization and statistical learning theory for data analysis
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Unsupervised video surveillance
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
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This paper describes a trainable and flexible system able to recognize visual dynamic events, e.g. movements performed by different people, from a stream of images taken by a fixed camera. Each event is represented by a feature vector built from the spatio-temporal changes detected in the observed image sequence. The system neither attempts to recover the 3D structure nor assumes a prior model of the observed dynamic events. During training a supervisor identifies and labels the events of interest among those automatically detected by the system. At run time, previously unseen events are detected and classified on the basis of the available examples. Several experiments on real images are reported and the benefits of using Support Vector Machines for performing effective classification from a relatively small number of labeled examples and for building noise tolerant representations are discussed. Preliminary results indicate that the proposed system can also be applied with equally good results to the case in which the dynamic events are gestures performed by different people.