An enhanced method for human action recognition

Journal of Advanced Research
Mona M MoussaHeba A El Nemr

Abstract

This paper presents a fast and simple method for human action recognition. The proposed technique relies on detecting interest points using SIFT (scale invariant feature transform) from each frame of the video. A fine-tuning step is used here to limit the number of interesting points according to the amount of details. Then the popular approach Bag of Video Words is applied with a new normalization technique. This normalization technique remarkably improves the results. Finally a multi class linear Support Vector Machine (SVM) is utilized for classification. Experiments were conducted on the KTH and Weizmann datasets. The results demonstrate that our approach outperforms most existing methods, achieving accuracy of 97.89% for KTH and 96.66% for Weizmann.

References

Jul 27, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Zhuolin JiangLarry S Davis

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Citations

Apr 15, 2017·International Journal of Intelligent Robotics and Applications·He Zhang, Cang Ye

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Methods Mentioned

BETA
feature extraction

Software Mentioned

SVM
SIFT
MoSIFT

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