I developed accurate 3D maps by implementing three local feature/matcher methods: LoFTR, DISK, and KeyNetAffNetHardNet. The competition aimed to reconstruct 3D scenes from multiple views, utilizing unstructured image collections available on the internet. This project has potential applications in photography, cultural heritage preservation, and various Google services, showcasing my expertise in Structure from Motion techniques and advancing to 3D modeling.
Leveraged AI to analyze players’ badminton training. By developing learned models of professional players’ shots and using perspective transformation and bird’s-eye view analysis, I revolutionized training by providing data-driven posture correction and precise performance assessment for amateur players based on distance, reachability, and other crucial metrics.
FitGen is a groundbreaking project that utilizes genetic algorithms and video pose analysis to create custom exercise plans based on individual body weight, height, exercise goals, and heart rate. By optimizing workout routines through real-time feedback on form, FitGen empowers users to achieve their fitness goals efficiently and safely by minimizing the risk of injury.
This project leverages TensorFlow and dlib for accurate age and gender estimation from facial images. By employing a Convolutional Neural Network (CNN) for deep learning, it enables simultaneous estimation of age and gender for multiple faces within an image, enhancing its applicability for diverse scenarios requiring demographic analysis.