Big Data Analytics for Processing Real-time Unstructured Data from CCTV in Traffic Management

Hamami, Faqih; Dahlan, Iqbal Ahmad; Prakosa, Setya Widyawan

doi:10.1109/icodsa50139.2020.9212858

Cited by 12 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, ITS benefits from the rapid advancements in several complementary fields. Sensors, cameras, and connected vehicles generate Big Data and analytics capabilities, enhancing our understanding of traffic flow, as highlighted in [62]. Furthermore, machine learning and artificial intelligence have recently experienced significant leaps.…”

Section: B Challenges and Opportunities In Conventional Itsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

show abstract

Section: B Challenges and Opportunities In Conventional Itsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Figure 3. Object detector model [15] This object detector model uses CSP Darknet 53 as the backbone; cross stage partial network (CSPNet) is a model that can improve CNN performance and significantly reduce the need for calculations on the system. CSPNet was created to mitigate the problems found in the previous backbone model wherein the previous model heavy computing was needed so that it could affect system performance [24], [25].…”

Section: Object Detectionmentioning

confidence: 99%

“…iii) This system produces a social distancing monitoring system for monitoring the crowd of people in the station environment using public CCTV security cameras to capture people crowd and social distancing situations [1], [2], [9], [11], [12]. vi) This paper can provide an informative dashboard that can provide descriptive analytics, predictive analytics, and prescriptive analytics to the stakeholders [13]- [15]. A real-time dashboard is presented for high-level executives to prevent violations.…”

Section: Introductionmentioning

confidence: 99%

Real-time passenger social distance monitoring with video analytics using deep learning in railway station

Dahlan

Putra

Supangkat

et al. 2022

IJEECS

Self Cite

View full text Add to dashboard Cite

<span lang="EN-US">Recently, at the end of December, the world faced a severe problem which is a pandemic that is caused by coronavirus disease. It also must be considered by the railway station's authorities that it must have the capability of reducing the covid transmission risk in the pandemic condition. Like a railway station, public transport plays a vital role in managing the COVID-19 spread because it is a center of public mass transportation that can be associated with the acquisition of infectious diseases. This paper implements social distance monitoring with a YOLOv4 object detection model for crowd monitoring using standard CCTV cameras to track visitors using the DeepSORT algorithm. This paper used CCTV surveillance with the actual implementation in Bandung Railway Station with the accuracy at 96.5 % result on people tracking with tested in real-time processing by using minicomputer Intel(R) Xeon(R) CPU E3-1231 v3 3.40GHz RAM 6 GB around at 18 FPS.</span>

show abstract

“…01, August 2023: pp. 10-18 highways, providing valuable insights for local authorities [4]. In the manufacturing sector, Industry 4.0, which encompasses technologies like big data, artificial intelligence, and the Internet of Things, has led to increased productivity and flexibility in production processes [5].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analysis and Its Utilization for Business Decision-Making

Ayuningtyas,

Mokodenseho,

Aziz

et al. 2023

WSIST

View full text Add to dashboard Cite

In today's data-driven business landscape, the analysis of big data has become a pivotal tool for making informed decisions across industries. This research method paper explores the methodologies employed in big data analysis for business decision-making and conducts a bibliometric analysis using VOSviewer to map the scholarly landscape of this field. The systematic literature review identifies key methodologies, including descriptive, predictive, and prescriptive analytics, text analysis, and network analysis. Real-world case studies demonstrate their practical applications in diverse sectors such as retail, finance, healthcare, and more. The bibliometric analysis unveils influential authors, collaborative networks, research clusters, and trends within the academic discourse on big data analysis and business decision-making. By integrating empirical research, systematic review, and bibliometric analysis, this paper offers a comprehensive understanding of the role of big data analysis in shaping contemporary business decisions.

show abstract

Big Data Analytics for Processing Real-time Unstructured Data from CCTV in Traffic Management

Cited by 12 publications

References 6 publications

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Real-time passenger social distance monitoring with video analytics using deep learning in railway station

Big Data Analysis and Its Utilization for Business Decision-Making

Contact Info

Product

Resources

About