Real-time Face and Object Detection with Age and Gender Prediction for Video Surveillance Applications

Abstract

Abstract: The development of advanced computer vision techniques has made it possible to perform real-time face and object detection for video surveillance applications. Video surveillance is an essential tool for monitoring public areas, buildings, and other locations for security and safety purposes. However, analyzing the vast amount of data generated by video surveillance systems can be challenging. Real-time face detection and object detection systems provide an effective solution to this problem, enabling the identification and tracking of people and objects of interest. In recent years, there has been a growing interest in incorporating age prediction and gender prediction into video surveillance systems. The ability to predict the age and gender of individuals can provide valuable insights to enhance the effectiveness of video surveillance applications. For example, age and gender prediction can be used to detect and prevent potential crimes. This research paper presents a study of a real-time face and object detection system with age and gender prediction for video surveillance applications. The proposed system utilizes deep learning and convolutional neural network techniques to achieve high accuracy in face and object detection, as well as age and gender prediction. The experimental results demonstrate the effectiveness of the proposed system in accurately detecting and tracking faces and objects while predicting their age and gender in real time. The proposed system has potential applications in various fields