Fundamentals of speech recognition
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Parametric Hidden Markov Models for Gesture Recognition
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The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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Reconstructing force-dynamic models from video sequences
Artificial Intelligence
Recognizing Human Actions: A Local SVM Approach
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Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
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A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Conditional Random Fields for Contextual Human Motion Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Diffusion Distance for Histogram Comparison
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-View Action Recognition from Temporal Self-similarities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Human action recognition using distribution of oriented rectangular patches
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Image and Vision Computing
People detection and stereoscopic analysis using MAS
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
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Computer Vision and Image Understanding
Modeling sense disambiguation of human pose: recognizing action at a distance by key poses
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
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Human action recognition by extracting features from negative space
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Exploring alternative spatial and temporal dense representations for action recognition
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MM '11 Proceedings of the 19th ACM international conference on Multimedia
Agents and computer vision for processing stereoscopic images
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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Pattern Recognition
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Journal of Visual Communication and Image Representation
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Image and Vision Computing
Human action recognition with salient trajectories
Signal Processing
Fast action recognition using negative space features
Expert Systems with Applications: An International Journal
Recognizing activities in multiple views with fusion of frame judgments
Image and Vision Computing
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Most of the approaches to human action recognition tend to form complex models which require lots of parameter estimation and computation time. In this study, we show that, human actions can be simply represented by pose without dealing with the complex representation of dynamics. Based on this idea, we propose a novel pose descriptor which we name as Histogram-of-Oriented-Rectangles (HOR) for representing and recognizing human actions in videos. We represent each human pose in an action sequence by oriented rectangular patches extracted over the human silhouette. We then form spatial oriented histograms to represent the distribution of these rectangular patches. We make use of several matching strategies to carry the information from the spatial domain described by the HOR descriptor to temporal domain. These are (i) nearest neighbor classification, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis to rectangular patches, (iii) a classifier-based approach using Support Vector Machines, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the HOR descriptor. For the cases when pose descriptor is not sufficiently strong alone, such as to differentiate actions ''jogging'' and ''running'', we also incorporate a simple velocity descriptor as a prior to the pose based classification step. We test our system with different configurations and experiment on two commonly used action datasets: the Weizmann dataset and the KTH dataset. Results show that our method is superior to other methods on Weizmann dataset with a perfect accuracy rate of 100%, and is comparable to the other methods on KTH dataset with a very high success rate close to 90%. These results prove that with a simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations.