Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2022

Abstract

This paper presents a real-time multi-view scheduling framework for DNN-based live video analytics at the edge to minimize frame processing latency. The work is motivated by applications where a higher frame rate is important, not to miss actions of interest. Examples include defense, border security, and intruder detection applications where sensors (in this paper, cameras) are deployed to monitor key roads, chokepoints, or passageways to identify events of interest (and intervene in real-time). Supporting a higher frame rate entails lowering frame processing latency. We assume that multiple cameras are deployed with partially overlapping views. Each camera has access to limited onboard computing capacity. Many targets cross the field of view of these cameras (but the great majority do not require action). We take advantage of the spatial-temporal correlations among multi-camera video streams to perform target-to-camera assignment such that the maximum frame processing time across cameras is minimized. Specifically, we use a data-driven approach to identify objects seen by multiple cameras, and propose a batch-aware latency-balanced (BALB) scheduling algorithm to drive the object-to-camera assignment. We empirically evaluate the proposed system with a real-world surveillance dataset on a testbed consisting of multiple NVIDIA Jetson boards. The results show that our system substantially improves the video processing speed, attaining multiplicative speedups of 2.45× to 6.85×, and consistently outperforms the competitive static region partitioning strategy.

Keywords

Edge Computing, Live Video Analytics, Collaborative Sensing

Discipline

Data Science | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2022 IEEE 42nd International Conference on Distributed Computing Systems, Bologna, Italy, July 10-13

First Page

503

Last Page

514

ISBN

9781665471787

Identifier

10.1109/ICDCS54860.2022.00055

Publisher

IEEE Computer Society

City or Country

Bologna, Italy

Embargo Period

6-25-2023

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICDCS54860.2022.00055

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