Making large-scale support vector machine learning practical
Advances in kernel methods
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Performance-driven motion choreographing with accelerometers
Computer Animation and Virtual Worlds - CASA' 2009 Special Issue
Action capture with accelerometers
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Tennissense: a platform for extracting semantic information from multi-camera tennis data
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Advances in view-invariant human motion analysis: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Accelerometer based gesture recognition using continuous HMMs
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Automated stroke classification in Tennis
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Using visual lifelogs to automatically characterize everyday activities
Information Sciences: an International Journal
Hi-index | 0.00 |
In this paper, we present a framework for both the automatic extraction of the temporal location of tennis strokes within a match and the subsequent classification of these as being either a serve, forehand or backhand. We employ the use of low-cost visual sensing and low-cost inertial sensing to achieve these aims, whereby a single modality can be used or a fusion of both classification strategies can be adopted if both modalities are available within a given capture scenario. This flexibility allows the framework to be applicable to a variety of user scenarios and hardware infrastructures. Our proposed approach is quantitatively evaluated using data captured from elite tennis players. Results point to the extremely accurate performance of the proposed approach irrespective of input modality configuration