Machine Learning
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
Cross-View Action Recognition from Temporal Self-similarities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Making action recognition robust to occlusions and viewpoint changes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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
Spatiotemporal bag-of-features for early wildfire smoke detection
Image and Vision Computing
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This paper addresses the multi-view action recognition problem with a local segment similarity voting scheme, upon which we build a novel multi-sensor fusion method. The recently proposed random forests classifier is used to map the local segment features to their corresponding prediction histograms. We compare the results of our approach with those of the baseline Bag-of-Words (BoW) and the Naive-Bayes Nearest Neighbor (NBNN) methods on the multi-view IXMAS dataset. Additionally, comparisons between our multi-camera fusion strategy and the normally used early feature concatenating strategy are also carried out using different camera views and different segment scales. It is proven that the proposed sensor fusion technique, coupled with the random forests classifier, is effective for multiple view human action recognition.