Towards improving quality of video-based vehicle counting method for traffic flow estimation

Xia, Yingjie; Shi, Xiaobo; Song, Guanghua; Geng, Qiaolei; Liu, Yuncai

doi:10.1016/j.sigpro.2014.10.035

Cited by 69 publications

(45 citation statements)

References 18 publications

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“…The objective in this phase is to detect moving vehicles in the scene. This operation is frequently based on the background subtraction (Yu et al, 2000;Mandellos et al, 2011;Xia et al, 2016) or Optical Flow (Ji et al, 2006;Johansson et al, 2009;Hossen and Tuli, 2016). In the systems based on background subtraction, the principal purpose of the algorithm is to model the background including stationary objects in the scene.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Moutakki

Ouloul

Afdel

et al. 2017

Transport and Telecommunication Journal

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The scope of this paper is a video surveillance system constituted of three principal modules, segmentation module, vehicle classification and vehicle counting. The segmentation is based on a background subtraction by using the Codebooks method. This step aims to define the regions of interest associated with vehicles. To classify vehicles in their type, our system uses the histograms of oriented gradient followed by support vector machine. Counting and tracking vehicles will be the last task to be performed. The presence of partial occlusion involves the decrease of the accuracy of vehicle segmentation and classification, which directly impacts the robustness of a video surveillance system. Therefore, a novel method to handle the partial occlusions based on vehicle classification process have developed. The results achieved have shown that the accuracy of vehicle counting and classification exceeds the accuracy measured in some existing systems.

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Section: Related Workmentioning

confidence: 99%

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Moutakki

Ouloul

Afdel

et al. 2017

Transport and Telecommunication Journal

View full text Add to dashboard Cite

show abstract

“…In the circumstances with incomplete prior data GMM assumes all the random process obeys indeterminate Gaussian distribution. Xia et al [6] proposed a GMM for vehicle segmentation for traffic flow estimation.…”

Section: Probabilistic Inference Identificationmentioning

confidence: 99%

“…In diverse transportation systems, the concerns about management efficiency and public safety bring forward high request to sensor networks [1][2][3]. To obtain more detailed and real-time information of traffic objects, varieties of powerful sensors have been invented and equipped, leading to information explosions [4][5][6]. For example, IMO (International Marine Organization) demands all the ports and vessels to equip Radar, AIS (Automatic Identification System) and satellite monitoring systems [7].…”

Section: Introductionmentioning

confidence: 99%

A Generalised Bayesian Inference Method for Maritime Surveillance Using Historical Data

Chu

et al. 2019

Symmetry

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In practice, maritime monitoring systems rely on manual work to identify the authenticities, risks, behaviours and importance of moving objects, which cannot be obtained directly through sensors, especially from marine radar. This paper proposes a generalised Bayesian inference-based artificial intelligence that is capable of identifying these patterns of moving objects based on their dynamic attributes and historical data. First of all, based on dependable prior data, likelihood information about objects of interest is obtained in terms of dynamic attributes, such as speed, direction and position. Observations on these attributes of a new object can be obtained as pieces of evidence profiled as probability distributions or generally belief distributions if ambiguity appears in the observations. Using likelihood modelling, the observed pieces of evidence are independent of the prior distribution patterns. Subsequently, Dempster’s rule is used to combine the pieces of evidence under consideration of their weight and reliability to identify the moving object. A real world case study of maritime radar surveillance is conducted to validate and prove the efficiency of the proposed approach. Overall, this approach is capable of providing a probabilistic and rigorous recognition result for pattern recognition of moving objects, which is suitable for any other actively detecting applications in transportation systems.

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“…A virtual loop-based method is proposed to improve the quality of vehicle counting method [18]. Background subtraction is used to detect the moving vehicles and an improved Gaussian mixture model.…”

Section: Video-analytics Applications For Transport Servicesmentioning

confidence: 99%

A Scalable IoT Video Data Analytics for Smart Cities

Doan¹,

Tran²,

Huynh³

et al. 2019

EAI Endorsed Transactions on Context-aware Systems and Applicat

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The smart city is a comprehensive application of information resources and a high degree of information technology integration. With the technical support from IoT (Internet of things), smart city need to have three features of being instrumented, interconnected and intelligent. IoT provides the ability to manage, remotely monitor and control devices from massive streams of real-time data.Our model offers a scalable IoT video data analytics applications for Smart cities to end users, who can exploit scalability in both data storage and processing power to execute analysis on large or complex datasets. This model provides data analytics programming suites and environments in which developers and researchers can design scalable analytics services and applications. A cloud/edge-based automated video analysis system to process large numbers of video streams, where the underlying infrastructure is able to scale based on the number of camera devices and easy to integrate analytic application. The system automates the video analysis process and reduces manual intervention. The design of our model is developed to be easily extended for new kinds of IoT devices, message routing and queueing, and data analytics, to permit specific application to be programmed via the paradigm to be flexible yet simple.

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Towards improving quality of video-based vehicle counting method for traffic flow estimation

Cited by 69 publications

References 18 publications

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling

A Generalised Bayesian Inference Method for Maritime Surveillance Using Historical Data

A Scalable IoT Video Data Analytics for Smart Cities

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