A Survey on Various Edge Detector Techniques

Bhardwaj, Shivam; Mittal, Ajay

doi:10.1016/j.protcy.2012.05.033

Cited by 139 publications

(60 citation statements)

References 8 publications

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“…The gradient based detectors [7] viz. Sobel, Robert, Prewitt, convolve the input image with their respective convolution mask to generate a gradient image.…”

Section: Edge Detection Methodologiesmentioning

confidence: 99%

Application of Edge Detection for Vehicle Detection in Traffic Surveillance System

Ashwini¹,

Yuvaraju²

2015

IJCA

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Detecting the vehicles and having a detailed behavior analysis of the vehicles and their behavior in a traffic surveillance system is an emerging area of research. Vehicle detection would be the first step to be addressed in this process. Various classes of vehicles are to be detected from the surveillance video and then they need to be classified based on various feature points. This paper brings out the different methods used for the vehicle detection from a video. An overview of the edge detection methodology is also given here, which is one of the methodologies used in vehicle detection.

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“…The gradient based detectors [7] viz. Sobel, Robert, Prewitt, convolve the input image with their respective convolution mask to generate a gradient image.…”

Section: Edge Detection Methodologiesmentioning

confidence: 99%

Application of Edge Detection for Vehicle Detection in Traffic Surveillance System

Ashwini¹,

Yuvaraju²

2015

IJCA

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“…Smoothing filter helps us to get the desired resullt in this case. We apply Gaussian smoothing filter [27] as presented in Eq. 1.…”

Section: Preprocessingmentioning

confidence: 99%

A histogram specification technique for dark image enhancement using a local transformation method

Hussain

Rahman

et al. 2018

IPSJ T Comput Vis Appl

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Traditional image enhancement techniques produce different types of noise such as unnatural effects, over-enhancement, and artifacts, and these drawbacks become more prominent in enhancing dark images. To overcome these drawbacks, we propose a dark image enhancement technique where local transformation of the pixels have been performed. Here, we apply a transformation method of different parts of the histogram of an input image to get a desired histogram. Afterwards, histogram specification technique has been done on the input image using this transformed histogram. The performance of the proposed technique has been evaluated in both qualitative and quantitative manner, which shows that the proposed method improves the quality of the image with minimal unexpected artifacts as compared to the other techniques.

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“…In [2] the Canny edge detector classifies a pixel as an edge if the amplitude of the gradient of the pixel is greater than that of the pixels of its two sides in the direction of the maximum intensity change. The proposed works show that the definition of edges in this way causes certain obvious edges to be missed.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Traffic Detection using Horizontal and Vertical Scanning

Chawla¹,

Moghe²

2017

IJARCSSE

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Traffic congestion on city road networks is one of the main issues to be addressed by today's traffic management schemes. Traffic congestion at times leads to delay in emergency services (i.e. Ambulance, Firefighter, Police, etc.) and most of the time causes inconvenience to commuters. In this paper a new traffic detection method is proposed which is based on the horizontal and vertical scanning of video frames to obtain accurate vehicle detection. Traffic congestion is measured in terms of traffic intensity which helps to ascertain whether the traffic at a given point is low, medium or high. The proposed method shows better result as compare to other methods in terms of accuracy.Keywords: Intelligent Traffic management, Emergency Vehicle, Morphological Operations, Horizontal and vertical scanning and video processing. I. INTRODUCTIONWith increase in population and corresponding increase in vehicular traffic leads to road congestion. Such traffic congestion leads to many major problems and challenges in the urban areas and populated cities. This causes wastage of time leading to missing opportunities, frustration and delay in reaching destination.Traffic loads depend heavily on parameters such as time, day, season, weather conditions and unpredictable situations such as accidents, special events or construction activities. If these parameters are not taken into account, the traffic control system will create delays. To solve the problem of congestion, new roads are built. The only disadvantage of making new roads on the facilities is that it makes the environment more congested. So, for that reason, it is necessary to change the system rather than make new infrastructure twice. A traffic control system can solve these problems by continuously detecting and adjusting the timing of traffic signals according to the actual traffic load such a system called as intelligent traffic control system. The advantages of building an intelligent traffic control system is to reduce congestion; operating costs; provide alternative routes for travelers, increase the capacity of the infrastructure.Most urban traffic is controlled by sensors and cameras which needs to be installed on major roads and streets. The existence of an automatic traffic detection system will assist in determining the traffic [1]. These systems extract information from the larger traffic issue and help us decide to improve traffic policy [3,4]. The goal of this current research is to develop an automatic vehicle counting system, which can process videos recorded from stationary cameras over roads e.g. CCTV cameras installed near traffic intersections / junctions and counting the number of vehicles passing a spot in a particular time for further collection of vehicle / traffic data. The paper aims to automate the traffic control system on highways and streets to determine Traffic Density , identify streets, roads in order to count the cars and monitoring of roads. Figure1 shows a sample image of urban traffic.

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A Survey on Various Edge Detector Techniques

Cited by 139 publications

References 8 publications

Application of Edge Detection for Vehicle Detection in Traffic Surveillance System

Application of Edge Detection for Vehicle Detection in Traffic Surveillance System

A histogram specification technique for dark image enhancement using a local transformation method

Real-Time Traffic Detection using Horizontal and Vertical Scanning

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