Literature Database Entry

chi2026edge-assisted

Xuejian Chi, Xiaoxi Zhang, Yifei Zou, Xingze Wu, Zhipeng Cai, Falko Dressler and Dongxiao Yu, "Edge-assisted Video Object Detection with Adaptive Resource Allocation and Model Selection," IEEE Transactions on Mobile Computing, 2026. (to appear)

Abstract

With the rise of applications such as facial recognition and intelligent monitoring, demand for video object detection has surged. To optimize detection performance, existing works primarily focus on either video transmission or processing independently, often overlooking small object detection, which is particularly difficult for resource-limited terminals. To address these limitations, we propose a solution that leverages edge servers to assist video object detection, enabling real-time and high-precision performance under resource constraints. We introduce Adaptive-EVOD, the first edge-assisted video detection system that optimizes communication and computation simultaneously through adaptive resolution and frame rate adjustment, resource allocation, and model selection strategies, with a particular emphasis on small object detection. Specifically, Adaptive-EVOD introduces three core innovations: (1) proposing a detection strategy for small objects to enhance precision; (2) employing deep reinforcement learning to dynamically determine detection location, resolution, and frame rate based on heterogeneous environments; and (3) applying Bayesian optimization to dynamically allocate GPU resources and select Low-Rank Adaptation (LoRA) ranks for detection models adaptively. Experiments on a real-world testbed show that Adaptive-EVOD reduces end-to-end latency by 5.7%–18.3% and improves detection accuracy by 7.8%–23.4% compared to state-of-the-art methods, which highlights the effectiveness of Adaptive-EVOD, particularly in complex and resource-constrained edge scenarios.

Quick access

BibTeX BibTeX

Contact

Xuejian Chi
Xiaoxi Zhang
Yifei Zou
Xingze Wu
Zhipeng Cai
Falko Dressler
Dongxiao Yu

BibTeX reference

@article{chi2026edge-assisted,
    author = {Chi, Xuejian and Zhang, Xiaoxi and Zou, Yifei and Wu, Xingze and Cai, Zhipeng and Dressler, Falko and Yu, Dongxiao},
    note = {to appear},
    title = {{Edge-assisted Video Object Detection with Adaptive Resource Allocation and Model Selection}},
    journal = {IEEE Transactions on Mobile Computing},
    issn = {1536-1233},
    publisher = {IEEE},
    year = {2026},
   }
   
   

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.

Last modified: 2026-08-01