Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Markerless motion capture of complex full-body movement for character animation
Proceedings of the Eurographic workshop on Computer animation and simulation
Individual recognition from periodic activity using hidden Markov models
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
On the Relationship of Human Walking and Running: Automatic Person Identification by Gait
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Motion-Based Recognition of People in EigenGait Space
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Articulated Soft Objects for Multiview Shape and Motion Capture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Temporal motion models for monocular and multiview 3D human body tracking
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
3D Periodic Human Motion Reconstruction from 2D Motion Sequences
Neural Computation
Computing and evaluating view-normalized body part trajectories
Image and Vision Computing
Person identification from human walking sequences using affine moment invariants
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Gait recognition based on the feature fusion
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Gait identification based on multi-view observations using omnidirectional camera
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Gradient-based hand tracking using silhouette data
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Matching gait image sequences in the frequency domain for tracking people at a distance
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Gait recognition using a view transformation model in the frequency domain
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Walker recognition without gait cycle estimation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Uniprojective features for gait recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
View independent human gait recognition using markerless 3d human motion capture
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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We propose an approach to gait analysis that relies on fitting 3-D temporal motion models to synchronized video sequences. These models allow us not only to track but also to recover motion parameters that can be used to recognize people and characterize their style. Because our method is robust to occlusions and insensitive to changes in direction of motion, our proposed approach has the potential to overcome some of the main limitations of current gait analysis methods. This is an important step towards taking biometrics out of the laboratory and into the real world.