Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance
The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/4/779 |
| _version_ | 1797603436115525632 |
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| author | Praveen Devadhas Sujakumari Paulraj Dassan |
| author_facet | Praveen Devadhas Sujakumari Paulraj Dassan |
| author_sort | Praveen Devadhas Sujakumari |
| collection | DOAJ |
| description | The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS. |
| first_indexed | 2024-03-11T04:29:09Z |
| format | Article |
| id | doaj.art-7f5200615f524ceaa0ece275c3e61874 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-11T04:29:09Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-7f5200615f524ceaa0ece275c3e618742023-11-17T21:32:36ZengMDPI AGSymmetry2073-89942023-03-0115477910.3390/sym15040779Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV SurveillancePraveen Devadhas Sujakumari0Paulraj Dassan1Department of Computer Science and Engineering, R.M.K. College of Engineering and Technology, Puduvoyal 601206, IndiaDepartment of Computer Science and Engineering, R.M.K. Engineering College, Kavaraipettai 601206, IndiaThe most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS.https://www.mdpi.com/2073-8994/15/4/779Intelligent Transportation System (ITS)image synthesisgenerative adversarial networks (GAN)traffic management systemHadoop Distributed File System (HDFS) |
| spellingShingle | Praveen Devadhas Sujakumari Paulraj Dassan Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance Symmetry Intelligent Transportation System (ITS) image synthesis generative adversarial networks (GAN) traffic management system Hadoop Distributed File System (HDFS) |
| title | Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance |
| title_full | Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance |
| title_fullStr | Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance |
| title_full_unstemmed | Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance |
| title_short | Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance |
| title_sort | generative adversarial networks gan and hdfs based realtime traffic forecasting system using cctv surveillance |
| topic | Intelligent Transportation System (ITS) image synthesis generative adversarial networks (GAN) traffic management system Hadoop Distributed File System (HDFS) |
| url | https://www.mdpi.com/2073-8994/15/4/779 |
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