Reconfigurable Cyber-Physical System for Critical Infrastructure Protection in Smart Cities via Smart Video-Surveillance

• URL: http://arxiv.org/abs/2011.14416v1
• Date: Sun, 29 Nov 2020 18:43:25 GMT
• Title: Reconfigurable Cyber-Physical System for Critical Infrastructure Protection in Smart Cities via Smart Video-Surveillance
• Authors: Juan Isern, Francisco Barranco, Daniel Deniz, Juho Lesonen, Jari Hannuksela, Richard R. Carrillo
• Abstract summary: We present a reconfigurable Cyber Physical System for the protection of CIs using distributed cloud-edge smart video surveillance. Our local edge nodes perform people detection via Deep Learning. Cloud server gathers results from nodes to carry out biometric facial identification, tracking, and perimeter monitoring.
• Score: 2.2509387878255818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated surveillance is essential for the protection of Critical Infrastructures (CIs) in future Smart Cities. The dynamic environments and bandwidth requirements demand systems that adapt themselves to react when events of interest occur. We present a reconfigurable Cyber Physical System for the protection of CIs using distributed cloud-edge smart video surveillance. Our local edge nodes perform people detection via Deep Learning. Processing is embedded in high performance SoCs (System-on-Chip) achieving real-time performance ($\approx$ 100 fps - frames per second) which enables efficiently managing video streams of more cameras source at lower frame rate. Cloud server gathers results from nodes to carry out biometric facial identification, tracking, and perimeter monitoring. A Quality and Resource Management module monitors data bandwidth and triggers reconfiguration adapting the transmitted video resolution. This also enables a flexible use of the network by multiple cameras while maintaining the accuracy of biometric identification. A real-world example shows a reduction of $\approx$ 75\% bandwidth use with respect to the no-reconfiguration scenario.

Related papers

• Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation [49.17930380106643]
  We propose a novel framework to speed up any architecture with skip-connections for real-time vision tasks. Specifically, at the arrival of each frame, we transform the features from the previous frame to reuse them at specific spatial bins. We then perform partial computation of the backbone network on the regions of the current frame that captures temporal differences between the current and previous frame.
  arXiv  Detail & Related papers  (2022-06-20T07:20:02Z)
• An End-to-End Integrated Computation and Communication Architecture for Goal-oriented Networking: A Perspective on Live Surveillance Video [0.0]
  We propose situation-aware streaming, for real-time identification of important events from live-feeds at the source. We show that the proposed scheme is able to reduce the required power consumption of the transmitter by 38.5% for 2160p (UHD) video.
  arXiv  Detail & Related papers  (2022-04-05T04:59:54Z)
• A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning [57.28467469709369]
  We investigate on designing a compact audio-visual wake word spotting (WWS) system by utilizing visual information. We introduce a neural network pruning strategy via the lottery ticket hypothesis in an iterative fine-tuning manner (LTH-IF) The proposed audio-visual system achieves significant performance improvements over the single-modality (audio-only or video-only) system under different noisy conditions.
  arXiv  Detail & Related papers  (2022-02-17T08:26:25Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Dynamic Slimmable Denoising Network [64.77565006158895]
  Dynamic slimmable denoising network (DDSNet) is a general method to achieve good denoising quality with less computational complexity. OurNet is empowered with the ability of dynamic inference by a dynamic gate. Our experiments demonstrate our-Net consistently outperforms the state-of-the-art individually trained static denoising networks.
  arXiv  Detail & Related papers  (2021-10-17T22:45:33Z)
• CANS: Communication Limited Camera Network Self-Configuration for Intelligent Industrial Surveillance [8.360870648463653]
  Realtime and intelligent video surveillance via camera networks involve computation-intensive vision detection tasks with massive video data. Multiple video streams compete for limited communication resources on the link between edge devices and camera networks. An adaptive camera network self-configuration method (CANS) of video surveillance is proposed to cope with multiple video streams of heterogeneous quality of service.
  arXiv  Detail & Related papers  (2021-09-13T01:54:33Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)
• VID-WIN: Fast Video Event Matching with Query-Aware Windowing at the Edge for the Internet of Multimedia Things [3.222802562733787]
  VID-WIN is an adaptive 2-stage allied windowing approach to accelerate video event analytics in an edge-cloud paradigm. VID-WIN exploits the video content and input knobs to accelerate the video inference process across nodes.
  arXiv  Detail & Related papers  (2021-04-27T10:08:40Z)
• Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning [1.1470070927586016]
  We propose an indoor monitoring reconfigurable CPS that uses embedded local nodes. Local processing at these nodes let us tackle some common issues: reduction of data bandwidth usage and preservation of privacy. Our proposal is an energy-aware system that triggers reconfiguration based on energy consumption for battery-powered nodes.
  arXiv  Detail & Related papers  (2020-10-07T16:05:09Z)
• Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection [99.94079901071163]
  This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs) We target applications where multiple sensors transmit data to a local processing unit, which executes a detection algorithm. Our detector is able to perform a detection every 450 ms, with an overall testing F1 score of 83%.
  arXiv  Detail & Related papers  (2020-04-03T17:36:26Z)
• CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge [1.5469452301122173]
  This paper introduces CONVINCE, a new approach to look at cameras as a collective entity that enables collaborative video analytics pipeline among cameras. Our results demonstrate that CONVINCE achieves an object identification accuracy of $sim$91%, by transmitting only about $sim$25% of all the recorded frames.
  arXiv  Detail & Related papers  (2020-02-05T23:55:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.