Withdraw
Loading…
360° and 2D video analytics in network and energy constrained environments
Civjan, Benjamin
Loading…
Permalink
https://hdl.handle.net/2142/129199
Description
- Title
- 360° and 2D video analytics in network and energy constrained environments
- Author(s)
- Civjan, Benjamin
- Issue Date
- 2025-04-15
- Director of Research (if dissertation) or Advisor (if thesis)
- Nahrstedt, Klara
- Department of Study
- Siebel School Comp & Data Sci
- Discipline
- Computer Science
- Degree Granting Institution
- University of Illinois Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Keyword(s)
- energy
- edge computing
- object detection
- 360-degree video
- video analytics
- firefighting
- Abstract
- Real-time video analytics enables rapid, automated content understanding, significantly reducing the time required to search through captured footage. However, many real-world scenarios that could benefit from real-time analytics face constraints that remain underexplored. This thesis focuses on one such use case: firefighter training. Firefighters rely on video for decision-making, post-mission feedback, and developing new training scenarios. However, deploying cameras in outdoor training environments presents two major challenges: limited network connectivity due to the distance from Wi-Fi infrastructure and restricted energy availability as cameras operate on battery power. To investigate network limitations, we conducted field tests at the Illinois Fire Service Institute (IFSI) to analyze connectivity from various locations on the grounds. Additionally, we developed a streaming framework supporting multiple video codecs (MJPEG, WebP, Tiled MJPEG, and H.264) and systematically evaluated their impact on bandwidth usage and 360° video streaming performance under real-world conditions at IFSI. To address the problem of energy-efficient video processing we developed a system, EcoLens, that dynamically optimizes processing configurations to minimize energy consumption of the camera while preserving essential video features for deep learning inference. We first conducted an extensive offline evaluation of various configurations comprising of device CPU frequency, frame filtering features, difference thresholds, and video bitrates, to establish apriori knowledge of their impact on energy consumption and inference accuracy. Leveraging this insight, we introduced an online system that employs multi-objective Bayesian optimization to intelligently explore and adapt configurations in real time. Our approach continuously refines processing settings to meet target inference accuracy with minimal edge device energy expenditure. Experimental results demonstrated the system’s effectiveness in reducing video processing energy use while maintaining high analytical performance, offering a practical solution for smart devices and edge computing applications.
- Graduation Semester
- 2025-05
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/129199
- Copyright and License Information
- Copyright 2025 Benjamin Civjan. All rights reserved.
Owning Collections
Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisManage Files
Loading…
Edit Collection Membership
Loading…
Edit Metadata
Loading…
Edit Properties
Loading…
Embargoes
Loading…