Dynamic scaling of video analytics for wide-area tracking in Urban spaces

Khochare, A and Sheshadri, KR and Shriram, R and Simmhan, Y (2019) Dynamic scaling of video analytics for wide-area tracking in Urban spaces. In: Proceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019, 14 May 2019 - 17 May 2019, Larnaca, pp. 76-81.

PDF CCGrid_2019.pdf - Published Version Restricted to Registered users only Download (18MB) | Request a copy

Abstract

Smart City deployments typically have thousands to even hundreds of thousands of Surveillance cameras. Rapid advancements in computer vision techniques due to Deep Neural Networks enable using these camera feeds for performing non-trivial analytics. Tracking a moving object of interest using a large network of cameras, also known as object reidentification, is one such analytic that empowers city administration with capabilities such as finding missing people or prioritizing emergency vehicles. We have built Anveshak, a framework for distributed wide-area tracking. Anveshak fills in the shortcomings of existing Big Data and Deep Learning frameworks by - exposing an intuitive and composable programming model; automating application deployment and orchestration across edge, fog and cloud resources and providing knobs to the user for managing the application performance. The knobs lend the application the ability to scale potentially to thousands of cameras. In this proposal we have designed two representative applications; missing person tracking and priority signalling for emergency vehicles. We empirically verify that the application scales to 1000 cameras on a Cloud-only deployment of 10 Azure VMs with 8 cores and 32GB RAM each. Alternatively, it scales to 500 cameras on a simulated setup of 100 edge, 30 fog, and 1 Cloud VM. We also highlight the effect of the knobs on the application performance. designed two representative applications; missing person tracking and priority signalling for emergency vehicles. We empirically verify that the application scales to 1000 cameras on a Cloud-only deployment of 10 Azure VMs with 8 cores and 32GB RAM each. Alternatively, it scales to 500 cameras on a simulated setup of 100 edge, 30 fog, and 1 Cloud VM. We also highlight the effect of the knobs on the application performance. © 2019 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2019
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc
Keywords:	Big data; Cameras; Cluster computing; Deep neural networks; Distributed parameter control systems; Emergency vehicles; Fog; Fog computing; Grid computing; Internet of things; Knobs; Network security, Application deployment; Application performance; Computer vision techniques; Data platform; Distributed stream processing; Learning frameworks; Surveillance cameras; Video analytics, Security systems
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	27 Dec 2022 10:55
Last Modified:	27 Dec 2022 10:55
URI:	https://eprints.iisc.ac.in/id/eprint/78591

Actions (login required)

View Item