CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
On the Estimation of Rigid Body Rotation from Noisy Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blink detection for real-time eye tracking
Journal of Network and Computer Applications
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Particle Filter with Analytical Inference for Human Body Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Automatic Recognition of Eye Blinking in Spontaneously Occurring Behavior
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Monitoring Head/Eye Motion for Driver Alertness with One Camera
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Binary Tree for Probability Learning in Eye Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Markov Chain Monte Carlo in small worlds
Statistics and Computing
Facial Action Coding Using Multiple Visual Cues and a Hierarchy of Particle Filters
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust facial landmark detection for intelligent vehicle system
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
On the roles of eye gaze and head dynamics in predicting driver's intent to change lanes
IEEE Transactions on Intelligent Transportation Systems
Eye/eyes tracking based on a unified deformable template and particle filtering
Pattern Recognition Letters
Blink and wink detection for mouse pointer control
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Adding facial actions into 3D model search to analyse behaviour in an unconstrained environment
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Computer Vision and Image Understanding
Detecting driver drowsiness using feature-level fusion and user-specific classification
Expert Systems with Applications: An International Journal
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We present a system that simultaneously tracks eyes and detects eye blinks. Two interactive particle filters are used for this purpose, one for the closed eyes and the other one for the open eyes. Each particle filter is used to track the eye locations as well as the scales of the eye subjects. The set of particles that gives higher confidence is defined as the primary set and the other one is defined as the secondary set. The eye location is estimated by the primary particle filter, and whether the eye status is open or closed is also decided by the label of the primary particle filter. When a new frame comes, the secondary particle filter is reinitialized according to the estimates from the primary particle filter. We use autoregression models for describing the state transition and a classification-based model for measuring the observation. Tensor subspace analysis is used for feature extraction which is followed by a logistic regression model to give the posterior estimation. The performance is carefully evaluated from two aspects: the blink detection rate and the tracking accuracy. The blink detection rate is evaluated using videos from varying scenarios, and the tracking accuracy is given by comparing with the benchmark data obtained using the Vicon motion capturing system. The setup for obtaining benchmark data for tracking accuracy evaluation is presented and experimental results are shown. Extensive experimental evaluations validate the capability of the algorithm.