Incident prediction by fuzzy image sequence analysis

Kimachi, Masatoshi; Kanayama, Kenji; Teramoto, Koji

doi:10.1109/vnis.1994.396867

Cited by 16 publications

(10 citation statements)

References 1 publication

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“…For specialized vehicle activities, such as detection of illegally parked vehicles and traffic accident vehicles that we are talking in this paper, have not been studied in depth with several possible exceptions of [15][16][17][18][19][20][21][22]. Paper [15,16] present two systems that detect and warn of illegally parked vehicles.…”

Section: B Vehicle Events Detectionmentioning

confidence: 99%

“…Paper [18] proposed a methodology for detecting this event in real-time by applying a novel image projection that reduces the dimensionality of the image data and thus reduces the computational complexity of the segmentation and tracking processes. There are several papers [19][20][21][22] related to accident vehicles detection by using machine vision techniques for surveillance. Paper [19] focus the attention on studying the abnormal behavior of vehicle causing an incident based on the concepts of fuzzy theory.…”

Section: B Vehicle Events Detectionmentioning

confidence: 99%

“…There are several papers [19][20][21][22] related to accident vehicles detection by using machine vision techniques for surveillance. Paper [19] focus the attention on studying the abnormal behavior of vehicle causing an incident based on the concepts of fuzzy theory. The decision whether an accident occurs or not relies on the behavioral abnormality of some continual image shots.…”

Section: B Vehicle Events Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic

Lan

Jiang

Fan

et al. 2015

J Sign Process Syst

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Obstacle detection in urban traffic is a hot topic in intelligent visual surveillance systems. In this paper, a realtime automatic obstacle recognition method based on computer vision technology is presented. The proposed method aims at detecting and recognizing the road obstacles such as abandoned objects, accident vehicles and illegally parked vehicles, which can prevent the traffic accident effectively. In the method, the target images are captured by a visible image sensor firstly. In order to avoid the static objects disappearing from foreground in short time when using GMM (Gaussian Mixture Model), background is built and foreground objects are extracted by the proposed algorithm SUOG (Selective Updating of GMM). Relative object speed is used to detect the static obstacles, and FROI (Flushed Region of Interest) algorithm based on the concept of connected domain, is presented to eliminate noises outside road and improve real-time capability. At last, a classification method of adaptive interested region based on HOG and SVM, and a new recognition algorithm of accident vehicles based on multi-feature fusion are proposed to classify the road obstacles. Experiments indicate that the detection rate of the proposed obstacle detection method is up to 96 % in urban road traffic. Through experiment, it is shown that the developed obstacle detection method has low computational complexity, and can fulfill the requirement of real-time applications, and it is correct and effective.

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Section: B Vehicle Events Detectionmentioning

confidence: 99%

Section: B Vehicle Events Detectionmentioning

confidence: 99%

Section: B Vehicle Events Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic

Lan

Jiang

Fan

et al. 2015

J Sign Process Syst

View full text Add to dashboard Cite

show abstract

“…Lane alert and assistance [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] Pedestrian detection Driver monitoring [38][39][40][41][42][43][44][45][46][47] Adaptive environmental sensing and control Efficiency Traffic flow Incident management 101,[113][114][115][116][117][118][119][120][121][122][123][124][125] Video-based tolling [126][127][128][129][130][131][132][133][134][135][136][137]…”

Section: Safetymentioning

confidence: 99%

Computer vision in roadway transportation systems: a survey

Loce

Bernal

et al. 2013

J. Electron. Imaging

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Abstract. There is a worldwide effort to apply 21st century intelli-gence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emis-sion reduction. Computer vision is playing a key role in this transpor-tation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges includ-ing imaging under a variety of environmental and illumination condi-tions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.22.4.041121]

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“…Placing sensors in the freeway performs the first task of data collection. Different types of sensors have been employed for collecting traffic data on freeways (Kimachi et al, 1994;Parkany and Bernstein, 1995;Ito et al, 1996;Wu and Adeli, 2001):…”

mentioning

confidence: 99%

Automatic detection of traffic incidents using data obtained from sensors embedded in intelligent freeways

Adeli

2002

Sensor Review

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This paper reviews innovative research done during the past few years on automatic detection of traffic incidents by the author and his associates using data obtained from sensors embedded in intelligent freeways. A multiparadigm intelligent system approach is employed to solve the complicated and chaotic pattern recognition problem using neural networks, fuzzy logic, and wavelets. Waveletbased de-noising and feature extraction techniques are employed to eliminate undesirable fluctuations in observed data from traffic sensors. The result is reliable algorithms with high incident detection and very low false alarm rates. Electronic accessThe research register for this journal is available at

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Incident prediction by fuzzy image sequence analysis

Cited by 16 publications

References 1 publication

Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic

Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic

Computer vision in roadway transportation systems: a survey

Automatic detection of traffic incidents using data obtained from sensors embedded in intelligent freeways

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