Company Filing History:
Years Active: 2012-2014
Title: Mat Cook: Innovator in Body Tracking Technology
Introduction
Mat Cook is a notable inventor based in Cambridge, GB. He has made significant contributions to the field of body tracking technology, holding 2 patents that showcase his innovative approach to detecting and tracking targets, including body parts and props.
Latest Patents
One of Mat Cook's latest patents is titled "Detection of body and props." This patent describes a system and method for detecting and tracking targets, including body parts and props. The technology acquires one or more depth images, generates classification maps associated with body parts and props, and utilizes a skeletal tracking system to track these elements. Additionally, it reports metrics regarding the tracked body parts and props, with feedback occurring between the skeletal tracking system and the prop tracking system. Another significant patent is "Proxy training data for human body tracking." This invention involves generating synthesized body images for a machine learning algorithm of a body joint tracking system. It retargets frames from motion capture sequences to various body types, ensuring a diverse set of images for training while avoiding redundancy. The use of a similarity metric helps identify distinct frames, and noise is added to depth images to enhance realism.
Career Highlights
Mat Cook is currently employed at Microsoft Technology Licensing, LLC, where he continues to develop innovative technologies in the realm of body tracking. His work has contributed to advancements in how machines understand and interpret human movement.
Collaborations
Mat has collaborated with notable colleagues, including Jamie Shotton and Andrew William Fitzgibbon, who have also made significant contributions to the field of computer vision and machine learning.
Conclusion
Mat Cook's innovative work in body tracking technology exemplifies the intersection of creativity and technical expertise. His patents reflect a commitment to advancing the capabilities of machine learning and human-computer interaction.