Video Analytics on a Mixed Network of Robust Cameras with Processing Capabilities

2018 ◽  
pp. 750-762
Author(s):  
Juan Pablo D‘Amato ◽  
Alejandro Perez ◽  
Leonardo Dominguez ◽  
Aldo Rubiales ◽  
Rosana Barbuzza ◽  
...  
Keyword(s):  
Video Analytics ◽  
Mixed Network

How to integrate video analytics into city centre CCTV monitoring

2006 ◽  
Author(s):  
N. Hewitson ◽  
R. Stead
Keyword(s):  
City Centre ◽  
Video Analytics

scholarly journals Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Aluizio Rocha Neto ◽  
Thiago P. Silva ◽  
Thais Batista ◽  
Flávia C. Delicato ◽  
Paulo F. Pires ◽  
...  
Keyword(s):  
Distributed System ◽  
Smart City ◽  
Large Scale ◽  
Facial Recognition ◽  
Public Spaces ◽  
Hardware Accelerators ◽  
Video Streams ◽  
Video Analytics ◽  
Learning Tasks ◽  
Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

Turkey Behavior Identification Using Video Analytics And Object Tracking

2021 ◽  
Author(s):  
Shengtai Ju ◽  
Marisa A. Erasmus ◽  
Fengqing Zhu ◽  
Amy R. Reibman
Keyword(s):  
Object Tracking ◽  
Video Analytics

Top-K Deep Video Analytics

2021 ◽  
Author(s):  
Ziliang Lai ◽  
Chenxia Han ◽  
Chris Liu ◽  
Pengfei Zhang ◽  
Eric Lo ◽  
...  
Keyword(s):  
Video Analytics

Towards cloud-edge collaborative online video analytics with fine-grained serverless pipelines

2021 ◽  
Author(s):  
Miao Zhang ◽  
Fangxin Wang ◽  
Yifei Zhu ◽  
Jiangchuan Liu ◽  
Zhi Wang
Keyword(s):  
Online Video ◽  
Video Analytics ◽  
Fine Grained

Distinguishing Between Parkinson’s Disease and Essential Tremor Through Video Analytics Using Machine Learning: a Pilot Study

2020 ◽  
pp. 1-1
Author(s):  
Ekaterina Kovalenko ◽  
Aleksandr Talitckii ◽  
Anna Anikina ◽  
Aleksei Shcherbak ◽  
Olga Zimniakova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Pilot Study ◽  
Essential Tremor ◽  
Video Analytics

scholarly journals Selecting Video Analytics using Cognitive Ergonomics: A Case Study for Operational Experimentation

2015 ◽  
Vol 3 ◽  
pp. 5245-5252 ◽  
Author(s):  
Nina M. Berry ◽  
William Prugh ◽  
Circe Lunkins ◽  
James Vega ◽  
Robin Landry ◽  
...  
Keyword(s):  
Video Analytics ◽  
Cognitive Ergonomics
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