Improved People Counting System Using Deep Learning

Abstract

Abstract: With the rapid rise in population, public areas such as malls, supermarkets, and transport hubs are becoming increasingly crowded. Businesses depending on customer footfallpatterns requireaccurate datatooptimize operations.Toaddress this, we developed a people counting and tracking system that detects, tracks, and identifies individuals in real-time.The system uses Faster R-CNN for robust people detection, offering high accuracy even in dense environments. To ensure consistent monitoring, DeepSORT assigns unique IDs to each individual andtracks them across frames. Additionally, DeepFace is integrated for face recognition, enabling the system tomatch detected faces with previously registered identities.A face registrationmodule (register_faces.py)allowswebcam- based registration, making it user-friendly. The evaluation module (evaluate.py) computes key performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The model was tested on a dataset comprising 2416 positive and 1218 negative image samples. It achieved a True Positive Rate (TPR) of 95.03%, a False Positive Rate (FPR) of 0.08%, and an overall accuracy of 97.08%. While the model performs well, challenges such as overlapping subjects, varying clothing, and lighting conditions may occasionally affect results. This system provides a reliable and scalable solution for people counting, face tracking, and identity verification