An enhanced method for human action recognition.
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.
Computers and Systems Department, Electronics Research Institute, Egypt.
Mona M
MM
Moussa
Computer Engineering Department, Faculty of Engineering, Cairo University, Egypt.
Elsayed
E
Hamayed
Computer Engineering Department, Faculty of Engineering, Cairo University, Egypt.
Magda B
MB
Fayek
Computers and Systems Department, Electronics Research Institute, Egypt.
Heba A
HA
El Nemr
Action recognition; Bag of words; SIFT; SVM