SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Detection and Tracking of Human Motion with a View-Based Representation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Motion segmentation and pose recognition with motion history gradients
Machine Vision and Applications - Special issue: IEEE WACV
The Representation and Recognition of Human Movement Using Temporal Templates
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)
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
View-Based Interpretation of Real-Time Optical Flow for Gesture Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Dynamic Models of Human Motion
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Space-Time Invariants and Video Motion Extraction from Arbitrary Viewpoints
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Free-viewpoint video of human actors
ACM SIGGRAPH 2003 Papers
Nonparametric Recognition of Nonrigid Motion
Nonparametric Recognition of Nonrigid Motion
A Reliable-Inference Framework for Recognition of Human Actions
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Towards a View Invariant Gait Recognition Algorithm
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Shape-From-Silhouette Across Time Part I: Theory and Algorithms
International Journal of Computer Vision
A bayesian approach to image-based visual hull reconstruction
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
View invariant activity recognition with manifold learning
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Pattern Recognition Letters
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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 introduce in this paper a novel method for employing image-based rendering to extend the range of applicability of human motion and gait recognition systems. Much work has been done in the field of human motion and gait recognition, and many interesting methods for detecting and classifying motion have been developed. However, systems that can robustly recognize human behavior in real-world contexts have yet to be developed. A significant reason for this is that the activities of humans in typical settings are unconstrained in terms of the motion path. People are free to move throughout the area of interest in any direction they like. While there have been many good classification systems developed in this domain, the majority of these systems have used a single camera providing input to a training-based learning method. Methods that rely on a single camera are implicitly view-dependent. In practice, the classification accuracy of these systems often becomes increasingly poor as the angle between the camera and the direction of motion varies away from the training view angle. As a result, these methods have limited real-world applications, since it is often impossible to limit the direction of motion of people so rigidly. We demonstrate the use of image-based rendering to adapt the input to meet the needs of the classifier by automatically constructing the proper view (image), that matches the training view, from a combination of arbitrary views taken from several cameras. We tested the method on 162 sequences of video data of human motions taken indoors and outdoors, and promising results were obtained.