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AdaMM: Adaptive Object Movement and Motion Tracking in Hierarchical Edge Computing System.

Jingyeom Kim1, Joohyung Lee1, Taeyeon Kim2

  • 1School of Computing, Gachon University, Seongnam 13120, Korea.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study introduces an adaptive framework for deep learning video surveillance, significantly reducing GPU memory usage by intelligently managing task execution. The system dynamically releases resources when not needed, optimizing performance and efficiency.

Keywords:
EdgeAIdeep learninghierarchical edge computingobject detection and trackingsoftware implementation

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Area of Science:

  • Computer Vision
  • Edge Computing
  • Artificial Intelligence

Background:

  • Deep learning (DL) for video surveillance demands substantial GPU resources (processing power and memory) for object tracking.
  • Unnecessary GPU memory allocation for DL models, even without objects, leads to resource wastage, especially for rare event detection.
  • Existing systems often keep DL models loaded, consuming significant GPU memory during idle periods.

Purpose of the Study:

  • To present a novel adaptive object movement and motion tracking (AdaMM) framework.
  • To reduce the GPU memory footprint of DL-based video surveillance services.
  • To optimize resource utilization in hierarchical edge computing systems.

Main Methods:

  • Categorized tasks (frame difference, object detection, motion tracking) by resource requirements.
  • Implemented a hierarchical edge system (1st edge: tasks 1 & 2, 2nd edge: task 3, cloud: alarms).
  • Developed adaptive triggering for motion tracking based on object detection and frame differences.

Main Results:

  • Achieved a maximum GPU memory reduction of 76.8% compared to baseline systems.
  • Demonstrated adaptive release of GPU memory for the motion tracking model.
  • Introduced a model loading delay of 2680 ms for object movement and motion tracking.

Conclusions:

  • The AdaMM framework effectively reduces GPU memory usage in DL-based video surveillance.
  • Hierarchical task management in edge computing optimizes resource allocation for surveillance.
  • The trade-off between memory savings and model loading delay is a key consideration.