Performance Evaluation of Video Analytics for Traffic Incident Detection and Vehicle Counts Collection

Kitae, Kim; Gutesa, Slobodan; Dimitrijević, B; Lee, Joyoung; Spasovic, Lazar N.; Mirza, Wasif; Singh, Jeevanjot

doi:10.1007/978-3-319-52081-0_9

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…A more recent trend is the application of data mining methods for images and videos with goals like image tagging and labeling as well as object detection and sematic segmentation (Kim et al, ; Liu, Chen, & Kubota, ). The main challenge is the handling of extremely large raw datasets, up to giga‐ and terabytes, caused by the high volume of examples.…”

Section: Criteria For Comparing Data Mining Softwarementioning

confidence: 99%

Data mining tools

Bartschat

Reischl

Mikut

2019

WIREs Data Min & Knowl

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The development and application of data mining algorithms requires the use of powerful software tools. With challenges such as big data encountered in economy or gene sequencing for life science, data mining is important for daily problems as well as specialized fields. However, the large variety of requirements and user groups lead to a huge number and diversity of software tools. We give an overview by discussing the historical development and presenting a range of existing state‐of‐the‐art data mining and related tools. This paper is an update of our previous article from 2011 following the encyclopedic aspect of Wiley Interdisciplinary Reviews to include new findings or references and changing outdated information. However, since the paper should be able to stand alone, it includes many still valid elements of the previous article. Following the original paper, we propose criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies. These criteria are then used to classify data mining tools into nine different categories. The typical characteristics of these types are explained and a selection of the most important tools is categorized. This article is categorized under: Application Areas > Data Mining Software Tools

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Section: Criteria For Comparing Data Mining Softwarementioning

confidence: 99%

Data mining tools

Bartschat

Reischl

Mikut

2019

WIREs Data Min & Knowl

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Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

Ang

Seng

Ngharamike

et al. 2022

IJGI

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With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications.

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Performance Evaluation of Video Analytics for Traffic Incident Detection and Vehicle Counts Collection

Cited by 2 publications

References 13 publications

Data mining tools

Data mining tools

Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

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