Fundamentals of speech recognition
Fundamentals of speech recognition
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing and Tracking Human Action
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Mosaic based representations of video sequences and their applications
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization
Survey of sports video analysis: research issues and applications
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
Volume Motion Template for View-Invariant Gesture Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Gesture spotting in continuous whole body action sequences using discrete hidden markov models
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Accurate and efficient gesture spotting via pruning and subgesture reasoning
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Directionally-grouped CHLAC motion feature extraction and its application to sport motion analysis
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
A ball tracking system for offline tennis videos
VIS'08 Proceedings of the 1st WSEAS international conference on Visualization, imaging and simulation
Tennis real play: an interactive tennis game with models from real videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Human gesture recognition plays an important role in automating the analysis of video material at a high level. Especially in sports videos, the determination of the player's gestures is a key task. In many sports views, the camera covers a large part of the sports arena, resulting in low resolution of the player's region. Moreover, the camera is not static, but moves dynamically around its optical center, i.e. pan/tilt/zoom camera. These factors make the determination of the player's gestures a challenging task. To overcome these problems, we propose a posture descriptor that is robust to shape corruption of the player's silhouette, and a gesture spotting method that is robust to noisy sequences of data and needs only a small amount of training data. The proposed posture descriptor extracts the feature points of a shape, based on the curvature scale space (CSS) method. The use of CSS makes this method robust to local noise, and our method is also robust to significant shape corruption of the player's silhouette. The proposed spotting method provides probabilistic similarity and is robust to noisy sequences of data. It needs only a small number of training data sets, which is a very useful characteristic when it is difficult to obtain enough data for model training. In this paper, we conducted experiments spotting serve gestures using broadcast tennis play video. From our experiments, for 63 shots of playing tennis, some of which include a serve gesture and while some do not, it achieved 97.5% precision rate and 86.7% recall rate.