CloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarios

• URL: http://arxiv.org/abs/2410.18399v1
• Date: Thu, 24 Oct 2024 03:27:05 GMT
• Title: CloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarios
• Authors: Huan Cui, Qing Li, Hanling Wang, Yong jiang,
• Abstract summary: CloudEye is a real-time, efficient mobile visual perception system. It uses content information mining on edge servers in a mobile vision system environment equipped with edge servers and coordinated with cloud servers. It reduces network bandwidth usage by 69.50%, increases inference speed by 24.55%, and improves detection accuracy by 67.30%.
• Score: 22.871591373774802
• License:
• Abstract: Mobile deep vision systems play a vital role in numerous scenarios. However, deep learning applications in mobile vision scenarios face problems such as tight computing resources. With the development of edge computing, the architecture of edge clouds has mitigated some of the issues related to limited computing resources. However, it has introduced increased latency. To address these challenges, we designed CloudEye which consists of Fast Inference Module, Feature Mining Module and Quality Encode Module. CloudEye is a real-time, efficient mobile visual perception system that leverages content information mining on edge servers in a mobile vision system environment equipped with edge servers and coordinated with cloud servers. Proven by sufficient experiments, we develop a prototype system that reduces network bandwidth usage by 69.50%, increases inference speed by 24.55%, and improves detection accuracy by 67.30%

Related papers

• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• High-Resolution Cloud Detection Network [4.717213036330225]
  This paper introduces the High-Resolution Cloud Detection Network (HR-cloud-Net) HR-cloud-Net integrates a high-resolution representation module, layer-wise cascaded feature fusion module, and multi-resolution pyramid pooling module. A novel approach is introduced wherein a student view, trained on noisy augmented images, is supervised by a teacher view processing normal images.
  arXiv  Detail & Related papers  (2024-07-10T04:54:03Z)
• Combining Cloud and Mobile Computing for Machine Learning [2.595189746033637]
  We consider model segmentation as a solution to improving the user experience. We show that the division not only reduces the wait time for users but can also be fine-tuned to optimize the workloads of the cloud.
  arXiv  Detail & Related papers  (2024-01-20T06:14:22Z)
• Streaming Video Analytics On The Edge With Asynchronous Cloud Support [2.7456483236562437]
  We propose a novel edge-cloud fusion algorithm that fuses edge and cloud predictions, achieving low latency and high accuracy. We focus on object detection in videos (applicable in many video analytics scenarios) and show that the fused edge-cloud predictions can outperform the accuracy of edge-only and cloud-only scenarios by as much as 50%.
  arXiv  Detail & Related papers  (2022-10-04T06:22:13Z)
• Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey [104.71816962689296]
  Unsupervised point cloud representation learning has attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using deep neural networks.
  arXiv  Detail & Related papers  (2022-02-28T07:46:05Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• Auto-Split: A General Framework of Collaborative Edge-Cloud AI [49.750972428032355]
  This paper describes the techniques and engineering practice behind Auto-Split, an edge-cloud collaborative prototype of Huawei Cloud. To the best of our knowledge, there is no existing industry product that provides the capability of Deep Neural Network (DNN) splitting.
  arXiv  Detail & Related papers  (2021-08-30T08:03:29Z)
• A Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning [31.712746462418693]
  This paper presents the first serverless system that takes full advantage of the client-fog-cloud synergy to better serve the DNN-based video analytics. To this end, we implement a holistic cloud-fog system referred to as V (Video-Platform-as-a-Service) The evaluation demonstrates that V is superior to several SOTA systems: it maintains high accuracy while reducing bandwidth usage by up to 21%, RTT by up to 62.5%, and cloud monetary cost by up to 50%.
  arXiv  Detail & Related papers  (2021-02-05T05:59:36Z)
• Anomaly Detection in a Large-scale Cloud Platform [9.283888139549067]
  Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. Service providers need to monitor the quality of their ever-growing offerings effectively. We designed and implemented an automated monitoring system for the IBM Cloud Platform.
  arXiv  Detail & Related papers  (2020-10-21T12:58:36Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)
• Deep Learning for 3D Point Clouds: A Survey [58.954684611055]
  This paper presents a review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets.
  arXiv  Detail & Related papers  (2019-12-27T09:15:54Z)

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.