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Frames: Problem Solving I

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Related Experiment Video

Updated: Jul 6, 2026

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

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Published on: December 25, 2017

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

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:

filtering frame processing rate long short-term memory real-time object detection scalability

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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.