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Wi-Filter: WiFi-Assisted Frame Filtering on the Edge for Scalable and Resource-Efficient Video Analytics.

Lawrence Lubwama1, Jungik Jang1, Jisung Pyo1

  • 1School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea.

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|February 13, 2025
PubMed
Summary
This summary is machine-generated.

Wi-Filter uses Wi-Fi signals and channel state information (CSI) to detect human motion, optimizing video analytics pipelines. This method significantly reduces false positives and enhances efficiency in intelligent surveillance systems.

Keywords:
1D CNNWi-Fi sensingchannel state informationedge computingvideo frame filtering

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Area of Science:

  • Computer Vision
  • Wireless Networking
  • Edge Computing

Background:

  • Large-scale intelligent surveillance systems face increased computational load from continuous video transmission.
  • Current solutions filter irrelevant frames at the edge, but dynamic optimization is needed.
  • Existing methods lack efficient motion detection for adaptive filtering.

Purpose of the Study:

  • To introduce Wi-Filter, a novel method for optimizing video analytics pipelines.
  • To leverage Wi-Fi signals and channel state information (CSI) for dynamic frame filtering.
  • To reduce the computational burden on real-time video analytics.

Main Methods:

  • Wi-Filter utilizes Wi-Fi channel state information (CSI) from wireless cameras.
  • Human motion detection is performed using CSI data to adjust filtering thresholds.
  • Motion-sensing models are trained via a self-supervised approach with synchronized camera feeds.

Main Results:

  • Achieved over 97.2% accuracy in motion detection.
  • Reduced false positive rates by up to 60% while maintaining high detection rates.
  • Demonstrated effectiveness in real-world experiments and challenging environments.

Conclusions:

  • Wi-Filter enhances the efficiency of intelligent video analytics pipelines.
  • Leveraging Wi-Fi CSI offers a promising approach for edge-based video analytics optimization.
  • The self-supervised training method enables robust motion detection for adaptive filtering.