Detection and Recognition of Periodic, Nonrigid Motion
International Journal of Computer Vision
View-Invariant Analysis of Cyclic Motion
International Journal of Computer Vision
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamical system representation, generation, and recognition of basic oscillatory motion gestures
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Describing motion for recognition
ISCV '95 Proceedings of the International Symposium on Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Finding Periodicity in Space and Time
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Extraction and Analysis of Multiple Periodic Motions in Video Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating 3D Trajectories of Periodic Motions from Stationary Monocular Views
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Representing cyclic human motion using functional analysis
Image and Vision Computing
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Structure from periodic motion
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
3D Reconstruction of Periodic Motion from a Single View
International Journal of Computer Vision
Reconstructing and analyzing periodic human motion from stationary monocular views
Computer Vision and Image Understanding
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Periodicity has been recognized as an important cue for tasks like activity recognition and gait analysis. However, most existing techniques analyze periodic motions only in image coordinates, making them very dependent on the viewing angle. In this paper we propose a new technique for reconstructing periodic point trajectories in 3D given only their apparent trajectories in image coordinates from a single stationary camera. We show that this reconstruction is possible without performing a costly gradient descent-type optimization, and is based only on a single SVD. This new algorithm is shown to accurately reconstruct natural human motions, allowing them to be compared in 3D world coordinates, independent of the angle from which they were originally viewed.