Missing Person Detection Using AI

Abstract

The focus of the planned study is on finding lost people in crowded environments including public events, festivals, temples, and meetings. In today's busy environments, single-person identification is a challenging endeavor. This problem is addressed by applying a deep learning idea to arrive at a workable solution. Individuals are recognized through the use of a Convolutional Neural Network (CNN). Several face characteristics are used to positively identify the missing person. The use of Face Detection is crucial to the success of this endeavor. The ImageNet Large-Scale Visual Recognition Challenge participant AlexNet is used. The main takeaway is that the model's depth is crucial to its excellent performance, which is computationally expensive but is made possible by the use of graphics processing units (GPUs) during training. With sufficient training using a wide variety of images, it is possible to locate the sought-after object in the allotted space. The project's momentum is based on watching the live feed. Faces are extracted from the video and saved to a database. A collection of photos is available for use in making identifications. We use our own dataset to train the AlexNet's many layers. The images in the database are labelled according to whether or not they contain a person by utilizing the pretrained network. Additionally, the KLT method should be used to determine the person's location and enable real-time tracking.