Object Detection and Recognition in Dark Using Feature Enhancement Algorithm

Computer vision makes extensive use of object recognition, which is essential for many applications. Object recognition approaches have now evolved into his two major various computer vision techniques. Object detection methods are described in this article. First, get an introduction and overview of current techniques in machine learning. Categories of classical machine learning using deep learning techniques. In everyday life, it is usual to use video cameras to monitor the campus. The majority of these surveillance methods employ people to keep an eye on what is happening in the target region. Yet, employing humans for monitoring has drawbacks of its own. To get around this restriction, researchers are developing automated visual surveillance systems. Environment modelling, motion segmentation, object categorization, tracking, behavior comprehension, person identification, and data fusion are the phases that make up the visual surveillance process. Finding moving objects in a video sequence is the first and most important step in visual surveillance. A person, a car, or another item may be the moving object of interest. The technology known as object detection is concerned with determining the semantic class of the moving item in the video sequence. Hence, Object Detection is crucial for following moving objects and analyzing their behavior in the provided video sequence. This study discusses the many object detection techniques that are available, taking into account the significance of object detection in visual surveillance.