Road Traffic Anomaly Detection Based on Fuzzy Theory

Li, Yanshan; Guo, Tianyu; Xia, Rongjie; Xie, Weixin

doi:10.1109/access.2018.2851747

Cited by 27 publications

(17 citation statements)

References 8 publications

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“…There are five membership functions, which process the linguistic variables to generate a meaningful output. Another research in a similar domain has focused on anomaly detection in road traffic scenarios using fuzzy theory [50]. The traffic flow, density, and the targets' motion state are designed on the basis of virtual detection lines, pixel statistics, and vehicle trajectory using fuzzy logic, correspondingly, which are fused together for optimal output.…”

Section: Fuzzy Logic Based Methods For Surveillance Bvdmentioning

confidence: 99%

“…For instance, a fuzzy logic based multi-object tracker is presented by Liang-qun et al [49], where a knowledge-based fuzzy inference system is designed via a set of fuzzy if-then rules known beforehand to improve the performance of multi-object tracking. Li et al [50] considered fuzzy theory to deal with road traffic anomaly detection. A risk assessment analysis system using surveillance videos is proposed in [47], where fuzzy cognitive maps are used to report on risky situations.…”

Section: Literature Reviewmentioning

confidence: 99%

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Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers’ attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.

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Section: Fuzzy Logic Based Methods For Surveillance Bvdmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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“…However, this study does not consider non-congestion as an output and has described only testing the model in a simulation with simulated data, furthermore, the focus of the study is on highways and does not reflect an urban road network, which has very different characteristics. Another study [9], examined road traffic anomalies that contribute to congestion at a single junction using a one-way traffic video sequence. This study uses two data inputs: Traffic flow and traffic density.…”

Section: B Transport Applicationsmentioning

confidence: 99%

Modelling Road Congestion Using a Fuzzy System and Real-World Data for Connected and Autonomous Vehicles

Abberley

Crockett

Cheng

2019

2019 Wireless Days (WD)

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Road congestion is estimated to cost the United Kingdom £307 billion by 2030. Furthermore, congestion contributes enormously to damaging the environment and people's health. In an attempt to combat the damage congestion is causing, new technologies are being developed, such as intelligent infrastructures and smart vehicles. The aim of this study is to develop a fuzzy system that can classify congestion using a real-world dataset referred to as Manchester Urban Congestion Dataset, which contains data similar to that collected by connected and autonomous vehicles. A set of fuzzy membership functions and rules were developed using a road congestion ontology and in conjunction with domain experts. Experiments are conducted to evaluate the fuzzy system in terms of its precision and recall in classifying congestion. Comparisons are made in terms of performance with traditional classification algorithms decision trees and Naïve Bayes using the Red, Amber, and Green classification methods currently implemented by Transport for Greater Manchester to label the dataset. The results have shown the fuzzy system has the ability to predict road congestion using volume and journey time, outperforming both decision trees and Naïve Bayes.

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“…This model makes use of traffic surveillance data to detect anomaly in the traffic flow patterns and traffic density using vehicle detection and tracking. The traffic anomaly is detected based on the traffic speed, density, and trajectory and vehicle moment to generate a [14] fuzzy traffic density. An anomaly is detected if there is a sudden deviation in the specified parameters a grid based approach is employed to detect any anomalies in the vehicle trajectories.…”

Section: B Road Traffic Anomaly Detection Based On Fuzzy Theorymentioning

confidence: 99%