The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Kernel independent component analysis
The Journal of Machine Learning Research
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
Proceedings of the 24th international conference on Machine learning
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiview activity recognition in smart homes with spatio-temporal features
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
On efficient use of multi-view data for activity recognition
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Fusion of single view soft k-NN classifiers for multicamera human action recognition
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.