The project aims to create new products for sports analysis using Artificial Intelligence and Machine Learning optimised for edge devices. These products are a video editor and a drawing tool that can animate video in motion and identify and track objects. This adventure represents a challenge for us, as it is the first time we will apply deep learning to video.
Fluendo has a solid background in multimedia technologies and HW optimisation, and we will complement it with the latest advances in Computer Vision and Artificial Intelligence. Additionally, it will help us establish an Artificial Intelligence pipeline that can be reused and improved for future projects and products.
Given our emphasis on AI solutions, the MLOps workflow is a cornerstone asset for our company. It streamlines the intricate process of transitioning from data analysis to actionable AI solutions, accelerating our ability to validate and scale novel ideas precisely.
With such a structured and efficient pipeline, we can rapidly iterate on new concepts, validate them in real-world scenarios, and ensure they meet the highest quality and performance standards. This, in turn, enhances our competitive advantage, allows for quicker response to market shifts, and underpins our reputation as leaders in innovation. Investing in our MLOps infrastructure is equivalent to investing in our business's future-proofing and sustained growth.
An algorithm designed to identify and locate specific objects, such as a ball or person, or features within an image or video.
A method that computes pixel-level masks for each object is handy for visualization and visual effects.
Functionality is used to follow the movement of objects across consecutive frames. It can be needed to get tactical insight into any sport.
Groups multiple detected objects into distinct clusters based on their characteristics. It distinguishes players from different teams.
Enables accurate mapping of 2D pixel coordinates to 3D real-world coordinates. It is required for distance calculation.
Reducing the data copies between the CPU and the GPU will enhance the speed and responsiveness of the inference.
Where We Started:
A base model trained on a generic dataset with fundamental detection capabilities.
Sports Domain Adaptation & Improvement:
With Transfer Learning for domain adaptation, we meticulously refine our model to a target dataset, concentrating on the essential elements like the players and the ball.
Achieved an optimized model with refined and precise detection capabilities.