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Human activity recognition at a kilometer range using single-photon LiDAR
Optics Express
|March 18, 2026
Summary
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.
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.
