Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Human Action Recognition Using Salient Opponent-Based Motion Features
CRV '10 Proceedings of the 2010 Canadian Conference on Computer and Robot Vision
Action Recognition by Multiple Features and Hyper-Sphere Multi-class SVM
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we propose an efficient & intuitive algorithm for the design of feature vector quantization using space-time interest point in video surveillance. The performance of activity recognition is generally depend upon the quantity of significant features but with proper feature quantization one can delivered the same performance with less number of features. The basic characteristics of algorithm are discussed and demonstrated by experiment. It is scalable in nature and work efficiently under varying conditions. In an experiment section, we show that our novel feature quantization approach takes less number of features in compared to standard quantization, while delivering the same performance.