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Object Tracking for a Smart City Using IoT and Edge Computing.

Hong Zhang1, Zeyu Zhang2, Lei Zhang3

  • 1Image Processing Center, BeiHang University, XueYuan Road No. 37, HaiDian District, Beijing 100083, China. dmrzhang@buaa.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a new region proposal correlation filter algorithm for visual object tracking on Internet-of-Things (IoT) and edge computing devices. The lightweight approach enables accurate tracking with limited computing capacity and memory, fitting most IoT devices with smaller model sizes.

Keywords:
Internet-of-Thingsedge computinglightweight computingobject trackingsmart city

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Smart City Technology

Background:

  • Internet-of-Things (IoT) and edge computing are increasingly used in smart cities for distributed data processing.
  • Machine learning, particularly visual object tracking, faces challenges due to limited computing power and memory on edge devices.
  • Current methods often require compromising accuracy or model size for deployment on resource-constrained systems.

Purpose of the Study:

  • To enable efficient visual object tracking on lightweight computing platforms common in IoT and edge environments.
  • To propose a novel algorithm that addresses the constraints of limited capacity and memory.
  • To facilitate the integration of advanced tracking capabilities into smart city applications.

Main Methods:

  • Development of a new region proposal correlation filter algorithm tailored for edge devices.
  • Implementation of techniques to achieve tracking with limited computing capacity and memory.
  • Systematic evaluation of the proposed algorithm's performance and resource utilization.

Main Results:

  • The proposed techniques successfully fit within the constraints of most IoT devices.
  • The algorithm maintains a relatively high level of accuracy in visual object tracking.
  • The resulting model size is significantly smaller compared to existing methods.

Conclusions:

  • The developed lightweight object tracking algorithm is suitable for widespread adoption in IoT and edge computing scenarios.
  • This advancement supports the deployment of accurate and efficient visual tracking in smart city applications.
  • The research contributes to overcoming resource limitations for machine learning on embedded systems.