CROWD MONITORING AND CONTROLLING SYSTEM FOR HOMELAND SECURITY IMPROVEMENT - Ahmad Abubakar Mustapha; Mohamed Sirajudeen Yoosuf (Proceedings on Engineering Sciences)
In an era where security concerns are paramount, developing advanced technological solutions is crucial to enhance public safety in crowded environments. This study introduces an innovative Internet of Things (IoT) based device designed to detect facial emotions and identify weapons among individuals in real-time within crowded spaces. Our Proposed work is through the integration of IoT and machine learning algorithms, the device can detect and analyze facial emotions and weapons in crowded areas (among protesters), the model has been experimented and it achieved an mAP of 99.39% @0.50IOU while 98.69% @0.75IOU and overall mAP of 91.52%. This device offers a proactive approach to security monitoring, enabling swift responses to predict weapons and emotions in a crowd's suspicious activities and enhancing overall situational awareness.