Making large-scale support vector machine learning practical
Advances in kernel methods
Human motion analysis: a review
Computer Vision and Image Understanding
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation and pose recognition with motion history gradients
Machine Vision and Applications - Special issue: IEEE WACV
Recognition of human body motion using phase space constraints
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Dining Activity Analysis Using a Hidden Markov Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Efficient Visual Event Detection Using Volumetric Features
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
Kernel-based Recognition of Human Actions Using Spatiotemporal Salient Points
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Continuous Gesture Recognition using a Sparse Bayesian Classifier
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Minimal-latency human action recognition using reliable-inference
Image and Vision Computing
Real-time adaptive hand motion recognition using a sparse bayesian classifier
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Human action classification using SVM_2K classifier on motion features
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Recognition of human activities using SVM multi-class classifier
Pattern Recognition Letters
A real-time, multimodal, and dimensional affect recognition system
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
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In this paper, a human action recognition system is proposed. The system is based on new, descriptive 'temporal template' features in order to achieve high-speed recognition in real-time, embedded applications. The limitations of the well-known 'Motion History Image' (MHI) temporal template are addressed and a new 'Motion History Histogram' (MHH) feature is proposed to capture more motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. To further improve classification performance, we combine both MHI and MHH into a low dimensional feature vector which is processed by a support vector machine (SVM). Experimental results show that our new representation can achieve a significant improvement in the performance of human action recognition over existing comparable methods, which use 2D temporal template based representations.