360° and 2D video analytics in network and energy constrained environments

Civjan, Benjamin

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https://hdl.handle.net/2142/129199 Copy

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

Language

eng

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.

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