Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Human Motion Signatures: Analysis, Synthesis, Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Human Body Tracking with Auxiliary Measurements
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Complex Human Activity Recognition for Monitoring Wide Outdoor Environments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
View-Invariant Human Activity Recognition Based on Shape and Motion Features
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Exploring the Space of a Human Action
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Posture and Gesture Recognition using 3D Body Shapes Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
Proposal maps driven MCMC for estimating human body pose in static images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Hierarchical recognition of daily human actions based on continuous hidden Markov models
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Probabilistic posture classification for Human-behavior analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A survey on vision-based human action recognition
Image and Vision Computing
An efficient Bayesian framework for on-line action recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Human action recognition with MPEG-7 descriptors and architectures
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Recognizing human actions using NWFE-based histogram vectors
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Ambient Assisted Living system for in-home monitoring of healthy independent elders
Expert Systems with Applications: An International Journal
Fully automatic methodology for human action recognition incorporating dynamic information
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Fitting distal limb segments for accurate skeletonization in human action recognition
Journal of Ambient Intelligence and Smart Environments
Human sign recognition for robot manipulation
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Describing video contents in natural language
HYBRID '12 Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
Human action recognition using silhouette histogram
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
Human activity recognition using multi-features and multiple kernel learning
Pattern Recognition
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This paper presents a HMM-based methodology for action recogni-tion using star skeleton as a representative descriptor of human posture. Star skeleton is a fast skeletonization technique by connecting from centroid of target object to contour extremes. To use star skeleton as feature for action recognition, we clearly define the fea-ture as a five-dimensional vector in star fashion because the head and four limbs are usually local extremes of human shape. In our proposed method, an action is composed of a series of star skeletons over time. Therefore, time-sequential images expressing human action are transformed into a feature vector sequence. Then the fea-ture vector sequence must be transformed into symbol sequence so that HMM can model the action. We design a posture codebook, which contains representative star skeletons of each action type and define a star distance to measure the similarity between feature vec-tors. Each feature vector of the sequence is matched against the codebook and is assigned to the symbol that is most similar. Conse-quently, the time-sequential images are converted to a symbol posture sequence. We use HMMs to model each action types to be recognized. In the training phase, the model parameters of the HMM of each category are optimized so as to best describe the training symbol sequences. For human action recognition, the model which best matches the observed symbol sequence is selected as the recog-nized category. We implement a system to automatically recognize ten different types of actions, and the system has been tested on real human action videos in two cases. One case is the classification of 100 video clips, each containing a single action type. A 98% recog-nition rate is obtained. The other case is a more realistic situation in which human takes a series of actions combined. An action-series recognition is achieved by referring a period of posture history using a sliding window scheme. The experimental results show promising performance.