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Related Experiment Video

Updated: Mar 2, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors.

Jong Hyun Kim¹, Hyung Gil Hong², Kang Ryoung Park³

¹Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea. zzingae@dongguk.edu.

Sensors (Basel, Switzerland)

|May 9, 2017

Summary

This study introduces a novel method for accurate nighttime human detection using single visible light images. The approach leverages convolutional neural networks for improved surveillance system performance in low-light conditions.

Keywords:

convolutional neural network intelligent surveillance system nighttime human detection visible light image

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Area of Science:

Computer Vision
Artificial Intelligence
Surveillance Technology

Background:

Intelligent surveillance systems require accurate human detection, especially in low-light conditions.
Existing methods using visible light cameras struggle with nighttime detection.
Near-infrared (NIR) and thermal cameras have limitations such as illumination constraints and high costs, respectively.

Purpose of the Study:

To develop an effective method for nighttime human detection using visible light cameras.
To overcome the limitations of existing nighttime human detection techniques.
To enable accurate human detection in diverse environments using single images.

Main Methods:

A novel method utilizing a single image captured by a visible light camera at night.
Application of a convolutional neural network (CNN) for human detection.
Validation using a self-constructed database (DNHD-DB1) and two open databases (KAIST, CVC).

Main Results:

The proposed method demonstrates high accuracy in detecting humans in various nighttime environments.
Experimental results show excellent performance compared to existing human detection methods.
The system effectively handles challenges posed by low-light conditions.

Conclusions:

The developed convolutional neural network-based method offers a robust solution for nighttime human detection.
This approach enhances the capabilities of intelligent surveillance systems in challenging lighting conditions.
The method provides a cost-effective and efficient alternative to existing technologies.