This artifact comes from a coding assignment in my “Computer Vision” course where I implemented a feature tracking algorithm using OpenCV. The goal of the project was to detect and track objects across a series of video frames using algorithms such as Harris Corner Detection and the Lucas-Kanade tracker. I worked on this individually, and the project involved writing Python code that could identify strong corner points in the first frame of a video and track those points across subsequent frames. This hands-on implementation deepened my understanding of motion estimation and low-level image analysis techniques. I tested the algorithm on various clips, adjusting parameters and handling noise and occlusion challenges.

This artifact demonstrates my technical competence in computer vision and my problem-solving skills in debugging complex image processing tasks. I learned to creatively adjust feature detection parameters and optimize performance for real-time processing. The iterative nature of testing and refining the algorithm taught me humility as errors were frequent, and success often came from revisiting the fundamentals (and office hours). This project also helped me appreciate the power of visual data and its applications in fields such as robotics, surveillance, and autonomous vehicles. I developed patience and resilience while troubleshooting frame inconsistencies and false matches. Ultimately, this project showcases my ability to implement theoretical algorithms in practice, bridging the gap between coursework and real-world applications.