REAL TIME SUSPECT DETECTION AND TRACKING USING AI BASED SURVELIIENCE SYSTEM

Abstract: This project presents an AI-based intelligent system that integrates Natural Language Processing (NLP) and Computer Vision techniques to automate resume analysis and real-time surveillance monitoring. The system utilizes advanced machine learning and deep learning models such as Named Entity Recognition (NER), semantic similarity algorithms, Convolutional Neural Networks (CNN), and YOLO-based object detection to process both textual and visual data efficiently. It analyzes resumes to match job descriptions, identify skill gaps, and provide optimization recommendations, while in surveillance it focuses on detecting and identifying suspicious persons based on given image data and tracking them across video frames. The system compares live video input with pre-stored images to recognize individuals and assign unique tracking identities for continuous monitoring. By combining text analysis with image- based person recognition, the system improves accuracy, reduces manual effort, and enables faster decisionmaking. Overall, the proposed system provides an efficient, scalable, and real-time solution for recruitment optimization and intelligent surveillance applications.

Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Computer Vision (CV), Deep Learning (DL), Resume Analysis, Semantic Similarity, Named Entity Recognition (NER), Convolutional Neural Networks (CNN), YOLO (You Only Look Once).