Computer vision in roadway transportation systems: a survey

Loce, Robert P.; Bernal, Edgar A.; Wu, Wencheng; Bala, Raja

doi:10.1117/1.jei.22.4.041121

Cited by 52 publications

(31 citation statements)

References 147 publications

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“…Due to their versatility, camera systems in automobiles are used both for surveillance of the interior of the car as well as the surroundings (Loce et al 2013). The following section discusses these applications and explains the specifics of using camera systems for these.…”

Section: Applicationsmentioning

confidence: 99%

Automotive Camera (Hardware)

Punke¹,

Menzel²,

Werthessen³

et al. 2015

Handbook of Driver Assistance Systems

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Today's traffic environment, such as traffic and information signs, road markings, and vehicles, is designed for human visual perception (even if first approaches for automatic evaluation by electronic sensor systems in the vehicle exist -see chapter "▶ Intersection Assistance"). This is done by different shapes, colors, or a temporal change of the signals.It is therefore a good choice to use a system similar to the human eye for machine perception of the environment. Camera systems are ideal candidates as they offer a comparable spectral, spatial, and temporal resolution. In addition to the "replica" of human vision, specific camera systems can provide other functions, including imaging in infrared spectral regions for night vision or a direct distance measurement.This chapter covers details on specific applications of camera-based driver assistance systems and the resulting technical needs for the camera system. Use cases covering the outside and inside of the vehicle are shown. The basis of every camera system is the camera module with its main parts -the lens system and the image sensor. The underlying technology is described, and the formation of the camera image is discussed. Moving to the system level, basic camera architectures including mono and stereo systems are analyzed. The chapter is completed with a discussion of the calibration of camera systems. ApplicationsDue to their versatility, camera systems in automobiles are used both for surveillance of the interior of the car as well as the surroundings (Loce et al. 2013). The following section discusses these applications and explains the specifics of using camera systems for these.A first driver assistance system using a camera system was the so-called rear view camera. The driver is assisted by the display of a live video stream on a monitor system. Advanced functions using computer vision are, for example, used in the high-beam assist function. In these systems, the video image is not displayed, but a specific function is directly derived from the camera image.In addition, cameras are used in the interior of the vehicle. Here, two main functions are of importance. First, the driver monitoring for detecting the state of the driver and the driver's intention and, secondly, the use of camera systems in the context of an advanced man-machine interface for controlling functions, for example, by means of gesture and gaze control.

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Section: Applicationsmentioning

confidence: 99%

Automotive Camera (Hardware)

Punke¹,

Menzel²,

Werthessen³

et al. 2015

Handbook of Driver Assistance Systems

View full text Add to dashboard Cite

show abstract

“…The texture-based approach proposed by Bullkich et al 37 achieved the second highest average ðη þ ξÞ∕2 rate (76.49%), and the geometry-based and physical-based approaches obtained the average ðη þ ξÞ∕2 rates of 69.21 and 67.86%, respectively. [2][3][4][5] Virtual loops have been proposed as an alternative to inductive loops installed in the pavement, since they can emulate the operation of inductive loops, providing the same information as the inductive loops about the presence/absence of vehicles in the loop with more flexibility (e.g., simpler installation and maintenance, and flexible operation). Therefore, a quantitative analysis also was performed to evaluate the performance of each shadow detection method in the context of vehicle detection.…”

Section: Chromaticitymentioning

confidence: 99%

“…Recent traffic monitoring systems tend to use video cameras instead of more traditional traffic monitoring sensors (e.g., microwave or magnetic sensors), since video-based (or image-based) systems can provide important technological advantages. [2][3][4][5] Several camera-based systems have been proposed in the literature for traffic monitoring. Also, urban centers may already have installed cameras that can be used for monitoring the urban traffic, reducing installation costs.…”

Section: Introductionmentioning

confidence: 99%

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

Barcellos

Gomes

Scharcanski

2016

J. Electron. Imaging

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Shadow detection in camera-based vehicle detection: survey and analysis,"Abstract. The number of vehicles in circulation in modern urban centers has greatly increased, which motivates the development of automatic traffic monitoring systems. Consequently, camera-based traffic monitoring systems are becoming more widely used, since they offer important technological advantages in comparison with traditional traffic monitoring systems (e.g., simpler maintenance and more flexibility for the design of practical configurations). The segmentation of the foreground (i.e., vehicles) is a fundamental step in the workflow of a camera-based traffic monitoring system. However, foreground segmentation can be negatively affected by vehicle shadows. This paper discusses the types of shadow detection methods available in the literature, their advantages, disadvantages, and in which situations these methods can improve camera-based vehicle detection for traffic monitoring. In order to compare the performance of these different types of shadow detection methods, experiments are conducted with typical methods of each category using publicly available datasets. This work shows that shadow detection definitely can improve the reliability of traffic monitoring systems, but the choice of the type of shadow method depends on the system specifications (e.g., tolerated error), the availability of computational resources, and prior information about the scene and its illumination in regular operation conditions. Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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“…On the other hand, the newly emerged computer vision‐based optical rail surface spalling inspection has become popular in recent years . In this technique, images of rail surfaces are first obtained by cameras, and these images are then analyzed by computer vision algorithms to automatically identify and locate the spalling areas .…”

Section: Introductionmentioning

confidence: 99%

Rail surface spalling detection based on visual saliency

Zhu

et al. 2018

IEEJ Transactions Elec Engng

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The rail surface images captured by line-scan cameras are susceptible to nonuniform illumination, stray light, smoothness variations of the rail surface, and so on, which degrade the detection accuracy of the rail surface spalling. To solve this problem, we propose an optical rail surface spalling detection algorithm based on visual saliency. First, the rail surface area is located to eliminate the interference of the surrounding area. Then, a two-dimensional difference of Gaussian (2D DoG) filter is used to reduce the noise. The filtered images are processed by means of a block local contrast measure (BLCM) estimator, which enhances the contrast of the spalling areas and produces the saliency map. Finally, a threshold is applied to locate the spalling areas. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 93.5% and shows good robustness under nonuniform illumination and various rail surface smoothness conditions. Zhixin Hu (Non-member) is currently a Lecturer with the School of Mechanical and Vehicular Engineering, Nanchang Institute of Science and Technology, China. He is also pursuing the Ph.D. degree at the School of Mechanical and Electrical Engineering, Nanchang University. His research interests include measurement and control technology of optical mechatronics. Hongtao Zhu (Non-member) is now a Professor with the School of Mechanical and Electrical Engineering, Nanchang University. His research interests include measurement and control technology of optical mechatronics. Ming Hu (Non-member) received the M.Sc. degree

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Computer vision in roadway transportation systems: a survey

Cited by 52 publications

References 147 publications

Automotive Camera (Hardware)

Automotive Camera (Hardware)

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

Rail surface spalling detection based on visual saliency

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