Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2024

Abstract

We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 cameras on a single Jetson TX2 with a 66.6% increase in accuracy and a simultaneous 18x (1800%) gain in cumulative throughput (475 FPS), far outperforming competitive baselines.

Keywords

Edge AI, Machine Perception, Canvas-based Processing

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the IEEE Conference on Multimedia and Expo (ICME), Niagara Falls, Canada, 2024 July 15-19

First Page

1

Last Page

6

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

8-26-2024

Copyright Owner and License

Authors

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