Justin Luu | Arjun Vilakathara | Remi Lejeune Introduction Computer vision, especially image segmentation and object detection, is a rapidly evolving field with potential applications across various industries. In recent years, significant improvement has occurred in the development of algorithms and training methodologies, drawing from existing techniques to enhance both the accuracy and efficiency of image segmentation tasks. As a team interested in CV (Computer Vision), we've been intrigued by the recent surge in AI (Artifical Intellingence) applications and advancements. Our curiosity led us to explore the intersection of AI and gaming, specifically how AI technologies could affect players' experiences in tactical FPS (First-Person Shooters) like Valorant. The primary goal of aiming in games like Valorant is precision and speed—key factors that can significantly impact gameplay. Traditionally, aimbots have relied on extracting data directly from the game or server to pinpoint the locations of opposing players. These methods are effective for cheating, but our interest here isn't to optimize how to cheat, but to see if we can make something similar using deep learning computer vision techniques. In our exploration, we will assess how these computer vision techniques can be used to develop an aimbot. The focus will be on evaluating the speed of processing the quality of image segmentation and its effectiveness within the game. We aim to develop an aimbot that mimics playing the game like a human would. The first step of which would be to first dileneate between player models and the background in the games video.