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Privacy-Preserving Live Video Analytics for Drones via Edge Computing

Piyush NagasubramaniamDepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USAChen WuDepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USAYuanyi SunNeeraj KaramchandaniDepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USASencun ZhuDepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USAYongzhong HeSchool of Computer, Beijing Jiaotong University, Beijing 100044, China

2024en

ABI

Annotatsiya

The use of lightweight drones has surged in recent years across both personal and commercial applications, necessitating the ability to conduct live video analytics on drones with limited computational resources. While edge computing offers a solution to the throughput bottleneck, it also opens the door to potential privacy invasions by exposing sensitive visual data to risks. In this work, we present a lightweight, privacy-preserving framework designed for real-time video analytics. By integrating a novel split-model architecture tailored for distributed deep learning through edge computing, our approach strikes a balance between operational efficiency and privacy. We provide comprehensive evaluations on privacy, object detection, latency, bandwidth usage, and object-tracking performance for our proposed privacy-preserving model.

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Identifikatorlar

DOI: 10.3390/app142210254

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