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Filtering Empty Video Frames for Efficient Real-Time Object Detection.

Yu Liu1, Kyoung-Don Kang1

  • 1Department of Computer Science, State University of New York at Binghamton, 4400 Vestal Parkway East, Vestal, NY 13850, USA.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

L-filter, a new lightweight method, enhances real-time object detection by predicting and filtering empty video frames. This boosts frame processing rates and scalability for visual sensing applications.

Keywords:
filteringframe processing ratelong short-term memoryreal-time object detectionscalability

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning models excel at object detection but suffer from high latency and resource demands.
  • Real-time object detection is crucial for visual sensing but is hindered by computational complexity.

Purpose of the Study:

  • To introduce L-filter, a novel lightweight filtering method for enhancing real-time object detection.
  • To improve frame processing rates and scalability in object detection systems.

Main Methods:

  • Developed L-filter, a hybrid time series analysis technique to accurately predict empty video frames.
  • Implemented a strategy to conduct object detection only on non-empty frames, optimizing resource usage.

Main Results:

  • L-filter improved frame processing rates by 31-47% for single traffic video streams compared to state-of-the-art models.
  • Achieved processing of up to six concurrent video streams on a single GPU, exceeding 57 fps per stream.

Conclusions:

  • L-filter significantly enhances the efficiency and scalability of real-time object detection systems.
  • The method offers a practical solution for deploying complex object detection models in resource-constrained environments.