People Counting System Based on Head Detection using Faster RCNN from Both Images and Videos

Abstract

The People Counting System is an advanced computer vision application with the primary goal of accurately counting people in crowded or monitored areas. It has been designed to address the need for real-time and reliable head detection to count individuals in various environments like shopping malls, transportation hubs, stadiums, and public gatherings. The motivation behind developing this system stems from the growing demand for automated and efficient crowd monitoring in security, retail analytics, and crowd management scenarios. Traditional people counting methods often rely on simpler techniques such as motion detection or background subtraction. However, these methods may not deliver precise results in complex scenarios with crowded environments or occlusions. Their accuracy suffers due to the lack of fine-grained object recognition and tracking, making them time-consuming, error-prone, and costly. To overcome these limitations, automating the counting process with computer vision techniques proves to be a significant improvement in terms of accuracy and efficiency. The demand for an accurate and efficient people counting system becomes essential as traditional manual counting methods and existing automated techniques struggle to handle crowded and occluded scenarios effectively. In response to this need, the proposed system offers an effective and reliable solution for automated people counting in crowded environments. Leveraging computer vision technology, this system utilizes the cutting-edge Faster R-CNN, an object detection model, to detect and count individual heads in real-time. Focusing on head detection ensures precise counting while minimizing common errors related to double counting, often encountered in simpler counting methods. This system represents a substantial advancement over traditional approaches by accurately identifying and localizing heads even in densely crowded scenes where heads may be partially obscured or overlapping with other objects. Its high accuracy ensures minimal counting errors, which proves crucial in applications where precise counting is necessary for decision-making processes, such as occupancy management, security, or retail analytics. One of the key advantages of the system is its real-time capability enabled by Faster R-CNN, allowing continuous and instantaneous counting. This feature ensures an immediate response to changing crowd conditions, making it a valuable tool for effective crowd management and decision-making in various scenarios