论文标题
夜视监视的图像增强和对象识别
Image Enhancement and Object Recognition for Night Vision Surveillance
论文作者
论文摘要
对象识别是任何监视系统的关键部分。在进行监视的领域，确定入侵者和异物。与夜晚相比，使用传统摄像机在日光下使用传统相机的监视系统的性能要出色。夜间监视的主要问题是传统摄像机捕获的物体与背景的对比度很低，因为在可见光谱中没有环境光。由于这个原因，使用红外摄像头在低光条件下拍摄图像，并增强图像，以获得具有基于空间域的不同增强算法的较高对比度的图像。然后将增强图像发送到分类过程。分类是通过使用卷积神经网络进行的，然后是完全连接的神经元层。本文比较了实施不同增强算法后分类的准确性。
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.
