Image Enhancement and Object Recognition for Night Vision Surveillance

TL;DR

This paper improves night vision surveillance by enhancing infrared images with spatial domain algorithms and applying CNNs for object recognition, aiming to boost accuracy in low-light conditions.

Contribution

It introduces specific image enhancement algorithms for infrared night images and evaluates their impact on CNN-based object classification accuracy.

Findings

Enhanced images improve object recognition accuracy.

Certain enhancement algorithms outperform others in low-light conditions.

The proposed method increases surveillance reliability at night.

Abstract

Object recognition is a critical part of any surveillance system. It is the matter of utmost concern to identify intruders and foreign objects in the area where surveillance is done. The performance of surveillance system using the traditional camera in daylight is vastly superior as compared to night. The main problem for surveillance during the night is the objects captured by traditional cameras have low contrast against the background because of the absence of ambient light in the visible spectrum. Due to that reason, the image is taken in low light condition using an Infrared Camera and the image is enhanced to obtain an image with higher contrast using different enhancing algorithms based on the spatial domain. The enhanced image is then sent to the classification process. The classification is done by using convolutional neural network followed by a fully connected layer of neurons. The accuracy of classification after implementing different enhancement algorithms is compared in this paper.