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  3. Human Activity Recognition At A Kilometer Range Using Single-photon Lidar.
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  3. Human Activity Recognition At A Kilometer Range Using Single-photon Lidar.

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Human activity recognition at a kilometer range using single-photon LiDAR.

Sandor Plosz, Rachael Tobin, Aongus McCarthy

    Optics Express
    |March 18, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces single-photon LiDAR for human activity recognition, overcoming RGB limitations. The system achieves over 80% accuracy at long ranges, even in challenging conditions.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Sensor Technology

    Background:

    • Human activity recognition is crucial but faces challenges with traditional RGB methods.
    • RGB methods are computationally expensive, sensitive to lighting, and raise privacy issues.
    • Single-photon LiDAR offers a privacy-preserving, robust alternative for 3D imaging.

    Purpose of the Study:

    • To develop a fast, long-range human activity recognition system using single-photon LiDAR and deep learning.
    • To address data limitations by generating synthetic datasets.
    • To create and release novel datasets for single-photon LiDAR-based activity recognition.

    Main Methods:

    • Combined an eye-safe single-photon LiDAR system with a deep learning pipeline.
    • Generated synthetic datasets using motion capture data and 3D models.
  • Trained a recurrent neural network on depth-image sequences of six activities.

    • Main Results:

    • Achieved over 80% accuracy in human activity recognition, even in challenging scenarios.
    • Demonstrated the system's capability for continuous and fast inference.
    • Released two new long-range single-photon LiDAR datasets (325m and 1.4km).

    Conclusions:

    • Single-photon LiDAR combined with deep learning is effective for fast, long-range human activity recognition.
    • The approach overcomes limitations of traditional RGB sensing.
    • The developed system and datasets advance the field of privacy-preserving remote sensing.