A Novel Pedestrian Detector on Low-Resolution Images: Gradient LBP Using Patterns of Oriented Edges

Ahmed BOUDISSA  Joo Kooi TAN  Hyoungseop KIM  Takashi SHINOMIYA  Seiji ISHIKAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.12   pp.2882-2887
Publication Date: 2013/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2882
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
local binary patterns,  pedestrian detection,  object recognition,  support vector machine,  

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This paper introduces a simple algorithm for pedestrian detection on low resolution images. The main objective is to create a successful means for real-time pedestrian detection. While the framework of the system consists of edge orientations combined with the local binary patterns (LBP) feature extractor, a novel way of selecting the threshold is introduced. Using the mean-variance of the background examples this threshold improves significantly the detection rate as well as the processing time. Furthermore, it makes the system robust to uniformly cluttered backgrounds, noise and light variations. The test data is the INRIA pedestrian dataset and for the classification, a support vector machine with a radial basis function (RBF) kernel is used. The system performs at state-of-the-art detection rates while being intuitive as well as very fast which leaves sufficient processing time for further operations such as tracking and danger estimation.