VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction

Wang, Hanling, Li, Qing, Sun, Heyang, Chen, Zuozhou, Hao, Yingqian, Peng, Junkun, Yuan, Zhenhui, Fu, Junsheng and Jiang, Yong (2023) VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction. IEEE Journal on Selected Areas in Communications, 41 (1). pp. 90-106. ISSN 0733-8716

VaBUS_JSAC_Hanling_Wang_0331.pdf - Accepted Version

Download (11MB) | Preview
Official URL:


Edge-cloud collaborative video analytics is transforming the way data is being handled, processed, and transmitted from the ever-growing number of surveillance cameras around the world. To avoid wasting limited bandwidth on unrelated content transmission, existing video analytics solutions usually perform temporal or spatial filtering to realize aggressive compression of irrelevant pixels. However, most of them work in a context-agnostic way while being oblivious to the circumstances where the video content is happening and the context-dependent characteristics under the hood. In this work, we propose VaBUS, a real-time video analytics system that leverages the rich contextual information of surveillance cameras to reduce bandwidth consumption for semantic compression. As a task-oriented communication system, VaBUS dynamically maintains the background image of the video on the edge with minimal system overhead and sends only highly confident Region of Interests (RoIs) to the cloud through adaptive weighting and encoding. With a lightweight experience-driven learning module, VaBUS is able to achieve high offline inference accuracy even when network congestion occurs. Experimental results show that VaBUS reduces bandwidth consumption by 25.0%-76.9% while achieving 90.7% accuracy for both the object detection and human keypoint detection tasks.

Item Type: Article
Additional Information: Funding information: 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61972189) 10.13039/100018919-Major Key Project of Peng Cheng Laboratory (PCL) (Grant Number: PCL2021A03-1) Shenzhen Key Laboratory of Software Defined Networking (Grant Number: ZDSYS20140509172959989)
Uncontrolled Keywords: Bandwidth, Cameras, Collaboration, Image edge detection, Real-time systems, Streaming media, Visual analytics, edge-cloud collaborative computing, semantic compression, task-oriented communication system, video analytics
Subjects: G500 Information Systems
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 20 Dec 2022 15:18
Last Modified: 20 Dec 2022 15:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics