Local binary pattern-based on-road vehicle detection in urban traffic scene

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.

Hybrid neural network based on novel audio feature for vehicle type identification

“…Wang et al proposed an approach to detect vehicles from aerial images with different resolutions and perspectives in an approximate real-time manner 11 . Huang et al propose a simple and effective vehicle detection method based on local vehicle's texture and appearance histograms feed into clustering forests 12 . However, it is not possible to capture vehicle images clearly due to complex lighting and harsh weather.…”

Geometric invariant features for the detection and analysis of vehicle

“…Hassaballah et al [ 8 ], proposed a methodology for the detection of vehicle based on local binary pattern (LBP). The LBP is used to extract local structure of vehicle.…”

PyTorch Operations Based Approach for Computing Local Binary Patterns

“…In computer vision applications, one of the important feature extraction approaches is the LBP transform (Pietikäinen et al 2011;Pietikäinen 2010). Due to efficient and computationally lightweight structure, it has been used in various machine learning applications such as LBP network for face recognition (Xi et al 2016), facial expression recognition (Rahul, Kohli, and Agarwal 2020), image forgery detection (Alahmadi et al 2017), acoustic scene classification (Yang and Krishnan 2017), dermoscopic skin lesion image segmentation (Pereira et al 2020), Hyperspectral image classification (Tu et al 2019), on-road vehicle detection in urban traffic scene (Hassaballah, Kenk, and El-Henawy 2020).…”