Human Detection and Counting using YOLOV4

Abstract

Object detection is a fundamental problem in computer vision that involves detecting and localizing objects within an image. In recent years, convolutional neural networks (CNNs) have become the state-of-the-art approach for object detection tasks. YOLO (You Only Look Once) is a popular real-time object detection system that uses a single CNN to predict the bounding boxes and class probabilities for objects in an image. In this paper, we present a human detection and counting system using YOLO. Our system is trained on a large dataset of annotated images and achieves high accuracy and real-time performance. We evaluate our system on a public dataset and demonstrate its effectiveness in various scenarios. This paper focuses on developing a system for human detection and counting using YOLOv4 and Flask API. The system aims to address the increasing importance of human detection and counting in various fields such as social distancing, crowd management, and surveillance.