Visual features for ego-centric activity recognition
Girmaw Abebe Tadesse, Andrea Cavallaro · 2018
Wearable cameras, which are becoming common mobile sensing platforms to capture the environment surrounding a person, can also be used to infer activities of the wearer. In this paper we critically discuss features for ego-centric activity recognition using videos. These features can be learned from data or designed to effectively encode motion magnitude, direction and other dynamics. Features can be derived from optical flow, from the displacement of key-points or the intensity centroid. We also discuss how features are effectively filtered and fused for specific tasks. Features presented in this paper can also be applied to other wearable systems that use accelerometer and gyroscope data.