Action recognition in video by sparse representation on covariance manifolds of silhouette tunnels
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
A novel feature extraction method for face recognition under different lighting conditions
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
One-Sequence learning of human actions
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
Fast human action classification and VOI localization with enhanced sparse coding
Journal of Visual Communication and Image Representation
Human action recognition employing negative space features
Journal of Visual Communication and Image Representation
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Proceedings of the 10th European Conference on Visual Media Production
A template matching approach of one-shot-learning gesture recognition
Pattern Recognition Letters
Editor's Choice Article: Human activity recognition in videos using a single example
Image and Vision Computing
Matching mixtures of curves for human action recognition
Computer Vision and Image Understanding
Continuous human action recognition in real time
Multimedia Tools and Applications
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We present a novel action recognition method based on space-time locally adaptive regression kernels and the matrix cosine similarity measure. The proposed method uses a single example of an action as a query to find similar matches. It does not require prior knowledge about actions, foreground/background segmentation, or any motion estimation or tracking. Our method is based on the computation of novel space-time descriptors from the query video which measure the likeness of a voxel to its surroundings. Salient features are extracted from said descriptors and compared against analogous features from the target video. This comparison is done using a matrix generalization of the cosine similarity measure. The algorithm yields a scalar resemblance volume, with each voxel indicating the likelihood of similarity between the query video and all cubes in the target video. Using nonparametric significance tests by controlling the false discovery rate, we detect the presence and location of actions similar to the query video. High performance is demonstrated on challenging sets of action data containing fast motions, varied contexts, and complicated background. Further experiments on the Weizmann and KTH data sets demonstrate state-of-the-art performance in action categorization.