Title: Robust pedestrian detection in infrared images using rotation and scale invariant-based structure element descriptor
Authors: Rajkumar Soundrapandiyan; P.V.S.S.R. Chandra Mouli
Addresses: School of Computer Science and Engineering, VIT University, Vellore 632014, Tamil Nadu, India ' School of Computer Science and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
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.
Keywords: adaptive quantisation; infrared imagery; local binary pattern; pedestrian detection; rotation and scale descriptor; structure element descriptor.
International Journal of Signal and Imaging Systems Engineering, 2017 Vol.10 No.3, pp.157 - 167
Received: 25 Jul 2016
Accepted: 21 Apr 2017
Published online: 21 Aug 2017 *