A Window-based Edge Detection Technique for Measuring Road Traffic Parameters in Real-Time

Siyal, Mohammed Yakoob

doi:10.1006/rtim.1995.1028

Cited by 26 publications

(9 citation statements)

References 8 publications

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“…Detecting the vehicles based on edge detection ensures the least dependency on variations in lighting conditions, as the edges of a vehicle are less sensitive than its other features to lighting changes [3,4].…”

Section: Detecting Vehicles and Their Movementsmentioning

confidence: 99%

“…However, most of them are not suitable for real-time image processing applications as they require a huge amount of computing power [4,8,9]. We used the selectiveaveraging background method [3,10] to obtain and update the background picture. This novel background technique is based on the two commonly used background methods in image processing, the selective and averaging methods.…”

Section: Background Updatingmentioning

confidence: 99%

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Image Processing Techniques For Real-Time Qualitative Road Traffic Data Analysis

Siyal

1999

Real-Time Imaging

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Section: Detecting Vehicles and Their Movementsmentioning

confidence: 99%

Section: Background Updatingmentioning

confidence: 99%

Image Processing Techniques For Real-Time Qualitative Road Traffic Data Analysis

Siyal

1999

Real-Time Imaging

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“…This topic arises from the background that strip-like entities are typical objects of interest in many RS applications, and so the associated parallel edges can be extracted out as fundamental data for a variety of practical techniques, such as airport locating (Di et al 2008), river monitoring (Yoshida et al 2004), road planning (Fathy andSiyal 1995, Bong et al 2009) and urban investigation (Sithole andVosselman 2006, Ferraioli 2009). …”

Section: Introductionmentioning

confidence: 99%

Automatic extraction of parallel edges based on eigenvalue analysis and collateral expansion

Lin

Hyyppä

2011

International Journal of Remote Sensing

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In the research field of remote sensing (RS), automatic extraction of parallel edges (AEPE) usually serves as the kernel when implementing automatic recognition of strip-like objects, such as rivers, highways, canyons and canals. The traditional algorithms proposed for edge detection commonly assume the mathematical singularity of boundaries as their theoretical basis and are also required to be all-prevalent, but the associated results cannot deal with the desired collection of parallel-only edges in most specific applications of RS data. To solve this problem, this article advances a novel algorithm for AEPE, which proceeds with the output edges from the existing edge-extraction methods as targets. The new algorithm comprises the procedure of eigenvalue analysis with improvements for extracting line segments, the procedure of collateral expansion for removing the non-parallel line segments recursively, and finally the procedure of segment connection. Finally, experiments based on both RS panchromatic images and light detection and ranging (LiDAR)-rastered images validate the new AEPE algorithm, with an efficacy greater than 70% and an accuracy better than 90%.

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“…Edge detection based segmentation of tra c scene has the advantage of being 3 Author for Correspondence. less sensitive to variation of ambient lighting and shadows [6]. However, the combination of background di erencing and edge detection technique has the advantage of being able to eliminate stationary vehicles, shadows and the road markings, and is less sensitive to variation of ambient lighting [2].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Pipeline Based Multiprocessor System For Real-Time Measurement of Road Traffic Parameters

2000

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R eal-time measurement and analysis of road tra c¯ow parameters such as volume, speed and queue are increasingly required for tra c control and management. Image processing is considered as an attractive and¯exible technique for automatic analysis of road tra c scenes for the measurement and data collection of road tra c parameters. In this paper, the authors describe a novel image processing based approach for analysis of road tra c scenes. Combined background di erencing and edge detection techniques are used to detect vehicles and measure various tra c parameters such as vehicle count and the queue length. A RISC based multiprocessor system was designed to enable real-time execution of the authors algorithm. The multiprocessor system has nine processing modules connected in a parallel pipeline fashion. Results shows that the authors multiprocessor system is able to provide measurement of tra c parameters in real-time. Results are presented for real tests of our system by analysing tra c scenes on the highways of Singapore.

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A Window-based Edge Detection Technique for Measuring Road Traffic Parameters in Real-Time

Cited by 26 publications

References 8 publications

Image Processing Techniques For Real-Time Qualitative Road Traffic Data Analysis

Image Processing Techniques For Real-Time Qualitative Road Traffic Data Analysis

Automatic extraction of parallel edges based on eigenvalue analysis and collateral expansion

A Parallel Pipeline Based Multiprocessor System For Real-Time Measurement of Road Traffic Parameters

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