This research explores advanced computer vision algorithms for accurate distance estimation between objects and cameras using vision-based techniques. The study focuses on implementing and optimizing algorithms for object detection, tracking, and depth estimation in real-world scenarios.

The research was conducted at the Office of Research Innovation and Commercialization (ORIC-NUML) where I worked as a Research Assistant. The project involved developing innovative approaches to solve complex computer vision challenges, particularly in the domain of spatial understanding and depth perception using machine learning techniques.

The methodology involved implementing state-of-the-art deep learning models including convolutional neural networks (CNNs) and computer vision algorithms. The research contributes to the field by proposing novel approaches for enhancing accuracy in distance estimation, which has applications in autonomous vehicles, robotics, and augmented reality systems.

"The integration of advanced machine learning techniques with computer vision opens new possibilities for accurate spatial understanding in real-time applications, contributing significantly to the advancement of AI-driven visual perception systems."

This research was part of my academic work at NUML Islamabad, where I completed my Bachelor's degree in Computer Software Engineering. The project demonstrates the practical application of theoretical knowledge in solving real-world computer vision challenges.

The research findings contribute to advancing the field of computer vision and have potential applications in various industries including automotive, healthcare, and smart city technologies. This work represents a significant step towards more accurate and efficient AI-powered visual perception systems.