Real-time video surveillance based human fall detection system using hybrid haar cascade classifier

Human activity recognition is a burgeoning field with the aim of observing and understanding human actions, focusing on appearance and movement. The goal of this field is to develop advanced systems for creating lifelike models and facilitating interactions. Computer vision is the foundation, serving surveillance, robot learning, user interfaces, and human–computer interaction. CCTV advancements enable impactful applications like home nursing and elderly care through video surveillance. However, extracting behavior from varied camera sources remains challenging. This paper introduces a proof of concept: a cost-effective fall detection system using a 5MP Pi camera connected via MIPI. A three-stage hybrid Haar Cascade model coupled with video frame sequences, utilizing background subtraction, achieves 89.21% accuracy with 2.5% false positives and 2.0% false negatives, surpassing state-of-the-art methods. The system's core purpose is to accurately monitor human movement, particularly detecting falls, and in emergencies, alert family via SMS or Email for the safety of solitary elderly individuals having age 60 years and above.