Performance Analysis: Object Detection System for Low Illumination

Abstract

Abstract: Abnormal activity detection plays a very important role in surveillance applications. The existing research on surveillance for daytime has achieved better performance by detecting and tracking objects using deep learning algorithms. However, it is difficult to achieve the same performance for night vision mainly due to low illumination. Deep learning is a powerful machine learning technique in which the object detector automatically learns image features required for detection tasks. It is required to generate a model that detects objects under low illumination. The approach is to use thermal infrared images and detect external objects, if any, and classify whether it is human or animal in an isolation area. With the rapid growth of deep learning, more efficient techniques will be implemented to solve the problems of object detection using neural networks and deep learning