Real Time Activity Monitoring Using Deep Learning

Computer vision, a cornerstone in computer science, equips machines with the ability to interpret digital images and videos, enhancing their intelligence. Activity recognition, a vital domain within computer vision, involves automatic categorization of agent actions, with a focus on identifying suspicious activities in diverse contexts. In real-time safety monitoring, it proves crucial in identifying anomalies and potential security breaches. Automated human activity recognition significantly impacts video surveillance, benefiting indoor and outdoor environments. Intelligent surveillance systems acting as vigilant digital eyes, identifying abnormal patterns or behaviors. Sensors like cameras and cell phones underpin human activity detection, extending beyond surveillance to facilitate seamless human-computer interaction. There is a pressing need for a proactive shift in surveillance systems. Instead of reactive responses post-incident, there should be a focus on proactive identification of anomalies, enabling authorities to intervene before situations escalate, ensuring the safety of individuals and communities. The integration of automated human activity recognition into intelligent video surveillance systems represents a technological leap, enhancing our understanding of human behavior and providing tools for preemptive action, fostering a safer environment for all. Recognizing objects in diverse environments using YOLO3 and CNN represents a intricate task within the domains of computer vision and artificial intelligence. The integration of advanced technologies such as AI, ML, and deep learning has greatly strengthened video surveillance systems. These systems employ a combination of these technologies to distinguish suspicious activities from live footage tracking. However, the unpredictable nature of human behavior adds complexity to this distinction between suspicious and normal activities. To address this challenge, deep learning methods are applied in settings to detect suspicious behavior and alert authorities accordingly. The monitoring process involves analyzing consecutive frames extracted from videos. The framework is divided into two parts: the first calculates features from video frames, and the second utilizes these features to predict whether the observed activity is suspicious or normal.