PDES ON GRAPHS FOR SEMI-SUPERVISED LEARNING APPLIED TO FIRST-PERSON ACTIVITY RECOGNITION IN BODY-WORN VIDEO

This paper showcases the use of PDE-based graph methods for modern machine learning applications. We consider a case study of body-worn video classification because of the large volume of data and the lack of training data due to sensitivity of the information. Many modern artificial intelligence methods are turning to deep learning which typically requires a lot of training data to be effective. They can also suffer from issues of trust because the heavy use of training data can inadvertently provide information about details of the training images and could compromise privacy. Our alternate approach is a physics-based machine learning that uses classical ideas like optical flow for video analysis paired with linear mixture models such as non-negative matrix factorization along with PDE-based graph classification methods that parallel geometric equations from PDE such as motion by mean curvature. The upshot is a methodology that can work well on video with modest amounts of training data and that can also be used to compress the information about the video scene so that no personal information is contained in the compressed data, making it possible to provide a larger group of people access to these compressed data without compromising privacy. The compressed data retains information about the wearer of the camera while discarding information about people, objects, and places in the scene.

Automated summary

This paper demonstrates the use of PDE-based graph methods for modern machine learning applications, focusing on a case study of body-worn video classification. By utilizing physics-based machine learning with classical ideas like optical flow, along with linear mixture models and PDE-based graph classification methods, it allows for effective video analysis with modest training data while also protecting privacy.