Video‐based road traffic monitoring and prediction using dynamic Bayesian networks

Chaudhary, Shraddha; Indu, S.; Chaudhury, Santanu

doi:10.1049/iet-its.2016.0336

Cited by 19 publications

(14 citation statements)

References 25 publications

(29 reference statements)

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“…Vehicle detection and tracking have given rise to a series of strategies for traffic safety [23][24][25]. For example, Chaudhary et al [26] counted the number of vehicles based on inter-frame difference, computed the number of moving pixels along the road in the difference binary image, and derived the traffic flow from the number and ratio of moving pixels. Ramya and Rajeswari [27] relied on frame difference and background subtraction to detect moving vehicles, tracked vehicles in virtual coils, and counted the number of vehicles.…”

Section: Traffic Safety Strategiesmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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Vehicle detection and tracking are key aspects of intelligent transportation. However, the accuracy of traditional methods for vehicle detection and tracking is severely suppressed by the complexity of road conditions, occlusion, and illumination changes. To solve the problem, this paper initializes the background model based on multiple frames with a fixed interval, and then establishes a moving vehicle detection algorithm based on trust interval. The established algorithm can easily evaluate the background complexity based on regional information. After that, the authors pointed out that the correlation filtering, a classical vehicle tracking algorithm, cannot adapt to the scale changes of vehicles, due to the weak dependence of background information. Hence, a tracking algorithm that adapts to vehicle scale was designed based on background information. Finally, the proposed algorithms were proved feasible for detection and tracking moving vehicles in complex environments. The research provides a good reference for the application of computer vision in moving target detection.

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Section: Traffic Safety Strategiesmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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“…A video camera was utilized to predict and monitor real-time traffic using a dynamic Bayesian networks technique in [16]. In this computationally light method, the distribution of spatial interest and spatiotemporal interest points is classified using the Gaussian mixture model (GMM), and then, the dynamic Bayesian approach is used.…”

Section: Vision-based Traffic Control Methodsmentioning

confidence: 99%

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

2022

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The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.

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“…The Total Vehicles feature is the number of bounding boxes provided by the vehicle detector and evaluated for each video frame [10,13,20,21,24,38,39,40,41,42,43].…”

Section: Methodsmentioning

confidence: 99%

“…Choudhury et al [10] proposed a system based on Dynamic Bayesian Network (DBN). It was tested on three videos after extracting visual features such as the number of moving vehicles [11].…”

Section: Related Studiesmentioning

confidence: 99%

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Impedovo

Balducci

Dentamaro

et al. 2019

Sensors

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Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

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Video‐based road traffic monitoring and prediction using dynamic Bayesian networks

Cited by 19 publications

References 25 publications

Moving Vehicle Detection and Tracking Based on Video Sequences

Moving Vehicle Detection and Tracking Based on Video Sequences

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

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