Unsupervised video summarization using cluster analysis for automatic vehicles counting and recognizing

Rabbouch, Hana; Saâdaoui, Foued; Mraïhi, Rafaa

doi:10.1016/j.neucom.2017.04.026

Cited by 30 publications

(7 citation statements)

References 36 publications

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“…However, this application still had limitations when it had to deal with complex traffic and environmental condition [163]. Therefore, clustering analysis was used in image segmentation [164] and pattern recognition [153] to overcome these limitations. Important information about dynamic traffic processes, such as the instantaneous number of vehicles, their weight, speed, and distance among vehicles, was available for precise positioning detection.…”

Section: Intelligent Transportation System (Its)mentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

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Section: Intelligent Transportation System (Its)mentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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“…Video-based traffic detection of passing vehicles has been implemented with various techniques: MoG (mixed of Gaussian) [ 42 ] and frame-history background subtraction [ 43 ] can filter out the nonstatic multi-modal background (e.g., shaking leaves, swaying branches, shadows), but both can still result in the false detection of slow-moving or stopping vehicles, and overlapping vehicles (either from a single lane or adjacent lanes). Color-histogram clustering addresses the issue with overlapping vehicles, but, in turn, exhibits inaccuracy due to sudden changes in vehicle velocity, illumination changes, and color composition variation [ 44 ].…”

Section: Related Workmentioning

confidence: 99%

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Aliansyah

Shimasaki

Senoo

et al. 2021

Sensors

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Vision-based structural displacement methods allow convenient monitoring of civil structures such as bridges, though they are often limited due to the small number of measurement points, constrained spatial resolution, and inability to identify the acting forces of the measured displacement. To increase the number of measurement points in vision-based bridge displacement measurement, this study introduces a front-view tandem marker motion capture system with side-view traffic counting to identify the force-inducing passing vehicles on the bridge’s deck. The proposed system was able to measure structural displacement at submillimeter resolution on eight measurement points at once at a distance of 40.8–64.2 m from a front-view camera. The traffic counting system with a side-view camera recorded the passing vehicles from two opposing lanes. We conducted a 35-min experiment for a 25 m-span steel road bridge with hundreds of cars passing over it and confirmed dynamic displacement distributions with amplitudes of several millimeters when large vehicles passed.

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“…Vehicle recognize and traffic density estimation algorithms in traffic are of interest to many researchers such as Rabbouch [16] using cluster analysis for automatic vehicles counting and recognizing. Sanchez [17] proposed algorithm's accuracy is usually determined by comparing, on each video-sequence, the visual inspection count with the automatic count that each algorithm provides.…”

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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Unsupervised video summarization using cluster analysis for automatic vehicles counting and recognizing

Cited by 30 publications

References 36 publications

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

A Scalable IoT Video Data Analytics for Smart Cities

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