ReconSight
| dc.contributor.author | Ahmad Bilal Bhatti | |
| dc.contributor.author | Anns Ijaz | |
| dc.contributor.author | Muhammad Mohsnain Haider | |
| dc.date.accessioned | 2025-12-26T18:36:08Z | |
| dc.date.available | 2025-12-26T18:36:08Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This project focuses on the development of an advanced system for real-time object detection, tracking, and data retrieval, designed to enhance both efficiency and accuracy across various applications. By leveraging state-of-the-art deep learning frameworks such as YOLO and TensorFlow, along with powerful computer vision tools like OpenCV and DeepSORT, the system ensures precise object identification and seamless tracking in dynamic environments. To support scalability and real-time processing, the system integrates Flask and AWS, enabling optimized resource utilization and ensuring high-performance execution across different computing infrastructures. Additionally, it incorporates a robust academic timetable management feature, addressing scheduling complexities for students and administrators by offering automated scheduling, conflict resolution, and smart recommendations. Designed with a well-structured use case model, the system includes essential functionalities such as user authentication, image tracking, and efficient data search mechanisms. These elements contribute to a user-friendly and streamlined workflow, making the system highly adaptable to both academic and industrial applications. This work represents a significant step toward automating and enhancing real-time detection and tracking, offering practical, scalable, and intelligent solutions for a wide range of real-world applications. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/18057 | |
| dc.language.iso | en | |
| dc.publisher | UMT, Lahore | |
| dc.title | ReconSight | |
| dc.type | Thesis |