Image Enhancement and Object Recognition for Night Vision Surveillance
This work addresses surveillance system limitations at night for security applications, but it is incremental as it applies existing enhancement and classification techniques to infrared data.
The paper tackles the problem of low-contrast object recognition in night vision surveillance by enhancing infrared images with spatial domain algorithms and classifying them using a convolutional neural network, comparing the accuracy of different enhancement methods.
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.