Robust pedestrian detection in infrared images using rotation and scale invariant-based structure element descriptor
by Rajkumar Soundrapandiyan; P.V.S.S.R. Chandra Mouli
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 10, No. 3, 2017

Abstract: Pedestrian detection is a significant problem in infrared (IR) images that find varieties of applications in defense systems. The performance of the state-of-the-art of pedestrian detection methods in IR images still have abundant space for improvement towards accuracy. In this paper, a three-level filtering-based pedestrian block detection method is proposed. In addition, a rotation and scale invariant structure element descriptor (RSSED) is proposed for pedestrian detection in infrared (IR) images. To extract RSSED features, the pedestrian block detection result is encoded using local binary pattern (LBP). The LBP encoded image is quantised adaptively to four levels. Further, the proposed RSSED is used to generate the feature descriptor from the quantised image. Finally, support vector machine (SVM) is used to classify the objects in given IR image into pedestrian and non-pedestrian. The experimental results demonstrate that the proposed method performs effectively in pedestrian detection than the other methods.

Online publication date: Mon, 21-Aug-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your 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