Shadow detection in camera-based vehicle detection: survey and analysis

Barcellos, Pablo; Gomes, Vitor; Scharcanski, Jacob

doi:10.1117/1.jei.25.5.051205

Cited by 16 publications

(11 citation statements)

References 38 publications

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“…These features are then used to train an SVM classifier. Others are proposed motion classification [4], geometric features [5] [6] shadow detection [7] to detect vehicle and pedestrian using the monocular camera. Only the vehicles detection is implemented in this paper because there are almost cars in the road scenes.…”

Section: Related Workmentioning

confidence: 99%

“…Haar and Histogram of oriented gradient (HOG) gradient features are used in the object detection research, as they are in the vehicle detection research. To extract the features of HOG, we evaluate the edges operators and then ditching and discretizing the directions of the pixel intensities into a histogram [9], [1] and [6]. HOG features are presented excellent detection presentation and robust image features in a range of computer vision tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the conversion formula defined by Eq.1. [6] to take the value of the corresponding pixel in the grayscale image. Grayscale = 0.299 * R + 0.587 * G+ 0.114 * B…”

Section:  Preprocessingmentioning

confidence: 99%

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High-reliability Vehicle Detection and Lane Collision Warning System

Kortli¹,

Marzougui²,

Atri³

2018

IJWMT

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In the last two decades, developing Driving Assistance Systems for security has been one of the most active research fields in order to minimize traffic accidents. Vehicle detection is a vital operation in most of these applications. In this paper, we present a high reliable and real-time lighting-invariant lane collision warning system. We implement a novel real-time vehicles detection using Histogram of Oriented Gradient and Support Vector Machine which could be used for collision prediction. Thus, in order to meet the conditions of real-time systems and to reduce the searching region, Otsu's threshold method play a critical role to extract the Region of Interest using the gradient information firstly. Secondly, we use Histogram of Oriented Gradient (HOG) descriptor to get the features vector, and these features are classified using a Support Vector Machine (SVM) classifier to get training base. Finally, we use this base to detect the vehicles in the road. Two sets generated the training data of our system a set of negative images (non-vehicles) a set of positive images (vehicles), and the test is performed on video sequences on the road. The proposed methodology is tested in different conditions. Our experimental results and accuracy evaluation indicates the efficiency of your system proposed for vehicles detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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High-reliability Vehicle Detection and Lane Collision Warning System

Kortli¹,

Marzougui²,

Atri³

2018

IJWMT

View full text Add to dashboard Cite

show abstract

“…During the past decade, a variety of moving shadow elimination algorithms continually spring up. Usually, these methods are mainly divided into four categories: geometry-based methods [4], [22], texture-based methods [5], [29], [30], chromaticity-based methods [6], [31], and physical model-based methods [7]. Geometry-based methods assume that light source, object shape, and the ground plane are known, using the information of direction, size, and shape of shadows to detect shadows [8].…”

Section: Introductionmentioning

confidence: 99%

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

Zhang

Li³

et al. 2020

IEEE Access

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Moving shadow elimination plays an important role in the field of moving object detection. However, the accuracy of shadow elimination is an open question, due to illumination and complex texture. Furthermore, the problem of misclassification of moving object caused by shadow has also become increasingly serious. To address this problem, this paper presents a moving shadow elimination algorithm based on the fusion of multi-feature pattern, which can enhance the accuracy of moving object detection system. In this approach, a dual-channel HSV color space feature and a uniform extended scale invariant local ternary pattern (UESILTP) texture feature are synthesized to elimination shadow. It greatly overcomes the misjudgment of dark object by color feature and the false detection caused by inconspicuous texture characteristics of moving object. Meantime, a method of region growth is adopted to fill the existing cavities in the color space. Finally, qualitative and quantitative comparisons with state-of-the-art methods show that the algorithm is effective.

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“…Following the thriving development of computer vision technology and surveillance equipment in recent years, vision‐based traffic flow surveillance has become one of the key applications of the ITS [1–5]. Previous research has primarily focused on daytime traffic flow surveillance because daytime images are clearer, more image features can be used for vehicle detection; thus, the methods appropriate for daytime vehicle detection [6–15] are more diverse.…”

Section: Introductionmentioning

confidence: 99%

High‐accuracy vehicle lamp detection for real‐time night‐time traffic surveillance

Tsai

Chen

2020

IET intell. transp. syst

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Vehicle lamps are an important image feature of the night‐time vehicle detection algorithm. This study proposes a real‐time night‐time vehicle detection algorithm based on light attenuation characteristic analysis, which consists of vehicle lamp detection and pairing. For the detection phase, this study proposes an automatic dual‐threshold method to quickly extract the attenuation regions around bright objects. This method is highly adaptable and can accurately extract attenuation regions to identify vehicle lamps and strong reflections. For the pairing phase, this study uses the moving trajectories of vehicle lamps to complete preliminary pairing. Then, the vehicle lamp pairing is optimised using the relative vertical and horizontal lamp positions in continuous images. Pixel‐ and object‐based methods were used to verify the experimental vehicle lamp detection results. The results of both verification methods indicate that the proposed algorithm without morphological operation is superior to other algorithms. The accuracy of the pairing phase was >94.6% in scenes with multiple lamps, high‐driving speed, high traffic flow, and rain. Finally, the proposed night‐time vehicle detection algorithm can achieve a real‐time execution speed for images 960 × 720 pixels in size.

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Shadow detection in camera-based vehicle detection: survey and analysis

Cited by 16 publications

References 38 publications

High-reliability Vehicle Detection and Lane Collision Warning System

High-reliability Vehicle Detection and Lane Collision Warning System

A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

High‐accuracy vehicle lamp detection for real‐time night‐time traffic surveillance

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