A Probabilistic Sensor for the Perception and Recognition of Activities
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
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)
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
International Journal of Computer Vision
Informative Shape Representations for Human Action Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Proceedings of the 6th ACM international conference on Image and video retrieval
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Human Activity Recognition Based on $\Re$ Transform and Fourier Mellin Transform
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Learning instance-to-class distance for human action recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Discriminative Video Pattern Search for Efficient Action Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Video and image classification based on Instance-to-Class (I2C) distance attracted many recent studies, due to the good generalization capabilities it provides for non-parametric classifiers. In this work we propose a method for action recognition. Our approach needs no intensive learning stage, and its classification performance is comparable to the state-of-the-art. A smart organization of training data allows the classifier to achieve reasonable computation times when working with large training databases. An efficient method for organizing training data in such a way is proposed. We perform thorough experiments on two popular action recognition datasets: the KTH dataset and the IXMAS dataset, and we study the influence of one of the key parameters of the method on classification performance.