An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Video

Vargas, Manuel; Toral, S. L.; Barrero, F.; Milla, Jose Manuel

doi:10.1109/itsc.2008.4732541

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…Methods like Adaptive Background Modeling with Shadow Suppression [3], Background estimation for dynamic video scenes [4], An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Video [5] and advanced methods for detecting backgrounds are available. Since non calibrated camera is used, the accuracy of the estimated background is not of much importance.…”

Section: Background Detectionmentioning

confidence: 99%

Automated Traffic Signal Prediction from Surveillance Videos

Deeban.¹,

Abirami²

2012

IJCA

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The traffic signals available in the present are based on the static feed of time without considering the actual available traffic. This leads to a situation where vehicles wait unnecessarily in one of the lanes while the traffic flow is not up to the considerable amount in the other lane. This paper provides a system to monitor the traffic flow automatically in traffic signals where video cameras are fixed. The time feed is made dynamic and automatic by processing the live traffic videos. The time for the signal is determined by two main factors: based on the density of the vehicles and on the number of vehicles in the lane. These inputs are given to a proposed algorithm which determines the optimal time period for the signal. General TermsImage processing, Traffic signal automation. KeywordsDynamic traffic signal prediction, density of vehicles, vehicular count. INTRODUCTIONVehicular detection techniques have been under research since 1970. As the need for reducing the installation and maintenance cost increased, the development of computer vision and widearea detection systems have evolved. Comparison between loop based and camera based techniques was funded by Minnesota Department of Transportation in 1990. The test proved the benefits of the camera-based techniques without even accounting other intangible benefits offered.The various other benefits provided via camera surveillance are as follows:Several patterns for traffic can be predicted. Moreover, the data for the laying roads, flyover construction is available. Law: Signal violation, traffic irregularities can be monitored and penalized.Safe Driving:Presence of surveillance cameras at traffic intersections cause drivers to drive with safe driving habits. Previous WorkVehicle detection techniques appear to be the weakest link in traffic surveillance and control system [1]. Closed loop, supersonic and radar method are already available. The drawback of these equipments is that they use "blind" type of detection technology and they cannot provide full traffic scene information in a human visual perspective manner. While detecting through surveillance cameras, videos provide more information than other sensors. Therefore, traffic surveillance systems based on videos provide reliable and robust traffic state information. As a result, monitoring by stationary video camera is one of the best techniques for traffic processing.Various research projects to detect and track vehicles from stationary cameras have been carried out in the past decade. In traffic surveillance system, the evaluation of traffic conditions can be represented by the following parameters: traffic flow rate, average traffic speed, the length of queue, the number of vehicles and traffic density. Most of the proposed methods used to extract traffic information based on vehicle detection and tracking techniques. In these systems, robust and reliable vehicle detection and tracking is a critical step. Current WorkIn this paper, a novel method has been attempted to dynamically and automat...

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Section: Background Detectionmentioning

confidence: 99%

Automated Traffic Signal Prediction from Surveillance Videos

Deeban.¹,

Abirami²

2012

IJCA

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“…A common problem in urban areas is addressed in [5], which proposes an enhanced version of a sigma-delta background estimation method, which is optimized for urban traffic scenes that are frequently affected by vehicles moving slowly or temporarily stopped. On the other hand, in [6] authors address the problem of refining moving objects by processing the background subtraction results.…”

Section: Literature Review a Related Workmentioning

confidence: 99%

“…The main difficulties of these algorithms happen in situations with very slow traffic movement or temporarily stopped vehicles as they start blending with the conceptual background. A solution to this problem is suggested in [5], as well as is an alternative proposed in [3] to use time-varying background and foreground intrinsic images. Both studies achieved very good results in terms of successfully segmenting background and foreground.…”

Section: B Related Techniquesmentioning

confidence: 99%

Video processing techniques for traffic information acquisition using uncontrolled video streams

Loureiro

Rossetti

Braga

2009

2009 12th International IEEE Conference on Intelligent Transportation Systems

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Abstract-This paper reports on the first steps taken in search of a solution that uses public video streams available on the Internet to address the increasing need for monitoring transportation networks with the intent of returning added value to the community, either by allowing a better understanding of the network and its needs or by feeding applications with real-time information for various purposes, such as simulation, decision-making support and updated route guidance. After the introduction of the field, we present our findings from a survey that briefly describes several works with related studies and explain the algorithms that can be adopted to get relevant information from video streams. This is followed by an analysis of the issues that may arise and the best ways to address them. Next it reports on the results achieved so far, draws some conclusions on what has been done and suggests the next steps of our research.

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“…Real time incremental segmentation and tracking of vehicles [8] this system mounted a camera relatively low to ground to avoid spill over effects, tracks vehicles at low camera angles using stable features. An enhanced back ground estimation algorithm [9] proposed background subtraction using sigma delta filter.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detector Method for Complex Environments on Embedded Linux Platform

Shanthi¹,

Sirisha²

2013

IJCA

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Traffic surveillance and investigation is important to increase the safety on roads. Vehicle detection and tracking in road surveillance is classical task in computer vision and critical component in modern traffic monitoring system. Intelligent transport system (ITS) brings advanced computing, sensing and telecommunication technologies to transport related problems. Many traffic surveillance systems have been developed to detect traffic congestion. In this paper video traffic detection system is used to detect the vehicles and track the vehicle speed. This project is implemented using Open CV Image processing library. In implementing this project gray scale conversion, background subtraction, and morphological operations are used. This proposed system uses a webcam located in traffic surveillance system. It captures video stream and send to ARM9 microcontroller; it processes the information such as number of vehicles, vehicle speed and send to remote PC.

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An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Video

Cited by 13 publications

References 41 publications

Automated Traffic Signal Prediction from Surveillance Videos

Automated Traffic Signal Prediction from Surveillance Videos

Video processing techniques for traffic information acquisition using uncontrolled video streams

Vehicle Detector Method for Complex Environments on Embedded Linux Platform

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