Faculty
Engineering
Supervisor Name
Dr. Xianbin Wang
Keywords
IoT, Edge Computing, Computer Engineering, Software Engineering, Video Surveillance, Image Segmentation
Description
Edge computing is critical for intelligent and cost-effective Internet of Things (IoT) applications. In achieving edge-enabled video surveillance, three main limitations, i.e. storage, computational restrictions and high video analysis complexity, have to be overcome. In this project, image segmentation is implemented to reduce heavy resource requirements and improve accuracy on edge devices
Acknowledgements
I would like to express my gratitude to my supervisor, Dr. Xianbin Wang, for his support and guidance throughout the project. As well, thank you to Cesar Gomez, Ruitao Chen, and Sabin Bhandari for their encouragement.
The project was a part of Western University’s USRI program in partnership with Western Research and Scholarship@Western.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.
Document Type
Poster
Event Website
https://usri2021catriona.wordpress.com/
Real-Time Edge-Enabled Video Surveillance
Edge computing is critical for intelligent and cost-effective Internet of Things (IoT) applications. In achieving edge-enabled video surveillance, three main limitations, i.e. storage, computational restrictions and high video analysis complexity, have to be overcome. In this project, image segmentation is implemented to reduce heavy resource requirements and improve accuracy on edge devices
https://ir.lib.uwo.ca/usri/usri2021/researchoutputshowcase/41