RVM-based human action classification through Gabor and Haar feature extraction Online publication date: Fri, 18-Dec-2015
by S. Maheswari; P. Arockia Jansi Rani
International Journal of Computational Vision and Robotics (IJCVR), Vol. 6, No. 1/2, 2016
Abstract: Human action recognition plays a vital role in surveillance applications. Human action recognition is motivated by some of the applications such as video retrieval, video surveillance systems, human robot interaction, to interact with deaf and dumb people etc. The aim is to analyse the role of Adaboost in the process of recognising the human action by extracting the motion features using optical flow. Adaboost is a supervised learning method used to select the subset of frames with most discriminatory motion features. Saliency point computation is performed to assign a measure of interest to each visual unit. Mean shift algorithm is then used for tracking the objects. Gabor feature is the global feature that includes more detailed information of frequency and orientation. Haar feature is used to show the variation in the pixel. Relevance vector machine classification gives a probabilistic output through Bayesian inference. The proposed system reduces the computation time and provides a higher recognition rate in comparison with existing gentle boost-based recognition system.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com