Local binary pattern-based on-road vehicle detection in urban traffic scene
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Local binary pattern‑based on‑road vehicle detection in urban traffic scene M. Hassaballah1 · Mourad A. Kenk2 · Ibrahim M. El‑Henawy3 Received: 14 July 2018 / Accepted: 4 February 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract For intelligent traffic monitoring systems and related applications, detecting vehicles on roads is a vital step. However, robust and efficient vehicles detection is still a challenging problem due to variations in the appearance of the vehicles and complicated background of the roads. In this paper, we propose a simple and effective vehicle detection method based on local vehicle’s texture and appearance histograms feed into clustering forests. The interdependency of vehicle’s parts locations is incorporating within a clustering forests framework. Local binary pattern-like descriptors are utilized for texture feature extraction. Through utilizing the LBP descriptors, the local structures of vehicles, such as edge, contour and flat region can be effectively depicted. The align set of histograms generated concurrence with LBPs spatial for random sampled local regions are used to measure the dissimilarity between regions of all training images. Evaluating the fit between histograms is built in clustering forests. That is, clustering discriminative codebooks of latent features are used to search between different LBP features of the random regions utilizing the Chi-square dissimilarity measure. Besides, saliency maps built by the learnt latent features are adopted to determine the vehicles locations in test image. Effectiveness of the proposed method is evaluated on different car datasets stressing various imaging conditions and the obtained results show that the method achieves significant improvements compared to published methods. Keywords Intelligent traffic monitoring · Vehicle detection · Local binary pattern · Saliency map · Clustering forests
1 Introduction In computer vision, the ability to detect road vehicles in digital images is an important key to a large number of applications [1] ranging from real traffic surveillance, transportation, traffic management, and traffic flow control systems at an intersection to advanced driver assistance systems as reported in [2–4]. For instance, video surveillance analysis systems for urban traffic roads provide rapid effective * M. Hassaballah [email protected] * Mourad A. Kenk [email protected] 1
Computer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt
2
Mathematics Department, Faculty of Science, South Valley University, Qena 83523, Egypt
3
Computer Science Department, Faculty of Computers and Information, Zagazig University, Zagazig, Egypt
information producing in increased safety and traffic flow such as stranded vehicles, lane crossing, vehicles parked cross the roads, traffic congestion, count passing vehicles [5, 6] as well as determining the plate number, type, speed of vehicles and their direction or
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