Enhancing Child Safety and Security Through Integrated Technology for Urban Areas

Child abduction poses a significant and growing threat to children's safety worldwide, with an alarming rise in reported cases. This research project addresses this issue by developing an innovative alert system that leverages machine learning and embedded systems. Utilizing techniques such as convolutional neural networks (CNNs) for image recognition and Haar-Cascade classifiers for real-time video detection, our system examines instances of children being found alone and processes real-time data to detect potential abductions. By generating timely alerts for authorities and caregivers, the system enables proactive identification and response. In addition, the system manages datasets by matching missing and found children, informing guardians when a match is detected. By integrating with existing CCTV infrastructure, our system enhances the effectiveness of surveillance methods, providing a comprehensive solution to mitigate the risks associated with child abduction.