A Cloud-Based UTOPIA Smart Video Surveillance System for Smart Cities

Yoon, Chel-Sang; Jung, Hae-Sun; Park, Jong-Won; Lee, Hak-Geun; Yun, Chang-Ho; Lee, Yong Woo

doi:10.3390/app10186572

Cited by 9 publications

(2 citation statements)

References 32 publications

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“…is is why they analyze video compression and features from the unique perspective of MPEG standards, which offers both academics and industry proof to accomplish the collaborative compression of the video shortly. In [7], using MapReduce, the author has developed UTOPIA Smart Video Surveillance for smart cities. From their end, we were able to incorporate smart video surveillance into our middleware platform.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

He¹,

Gao²,

Zheng³

et al. 2021

Wireless Power Transfer

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Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.

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Section: Related Workmentioning

confidence: 99%

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

He¹,

Gao²,

Zheng³

et al. 2021

Wireless Power Transfer

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show abstract

“…The object identification and understanding within an ongoing video stream is based on the Computer Vision (CV) domain of real-time video analysis. Prominent examples for real-time object detection and analysis include Google Lens or smart city applications that perform video surveillance [3][4][5] or for connected autonomous cars, as illustrated in Figure 1. Especially for the latter, incorporating new sensor data such as from LIDAR and other on-board sensors that goes beyond image data alone is also attracting interest [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

2021

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With increasing numbers of computer vision and object detection application scenarios, those requiring ultra-low service latency times have become increasingly prominent; e.g., those for autonomous and connected vehicles or smart city applications. The incorporation of machine learning through the applications of trained models in these scenarios can pose a computational challenge. The softwarization of networks provides opportunities to incorporate computing into the network, increasing flexibility by distributing workloads through offloading from client and edge nodes over in-network nodes to servers. In this article, we present an example for splitting the inference component of the YOLOv2 trained machine learning model between client, network, and service side processing to reduce the overall service latency. Assuming a client has 20% of the server computational resources, we observe a more than 12-fold reduction of service latency when incorporating our service split compared to on-client processing and and an increase in speed of more than 25% compared to performing everything on the server. Our approach is not only applicable to object detection, but can also be applied in a broad variety of machine learning-based applications and services.

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A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

Siddiqui,

Siddiqi

2023

Communications in Computer and Information Science

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A Cloud-Based UTOPIA Smart Video Surveillance System for Smart Cities

Cited by 9 publications

References 32 publications

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization

You Only Look Once, But Compute Twice: Service Function Chaining for Low-Latency Object Detection in Softwarized Networks

A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

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