Improving the Utility of Egocentric Videos

For either entertainment or documenting purposes, people are starting to record their life using egocentric cameras, mounted on either a person or a vehicle. Our target is to improve the utility of these egocentric videos.

For egocentric videos with an entertainment purpose, we aim to enhance the viewing experience to improve overall enjoyment. We focus on First-Person Videos (FPVs), which are recorded by wearable cameras. People record FPVs in order to share their First-Person Experience (FPE). However, raw FPVs are usually too shaky to watch, which ruins the experience. We explore the mechanism of human perception and propose a biometric-based measurement called the Viewing Experience (VE) score, which measures both the stability and the First-person Motion Information (FPMI) of a FPV. This enables us to further develop a system to stabilize FPVs while preserving their FPMI. Experimental results show that our system is robust and efficient in measuring and improving the VE of FPVs.

For egocentric videos whose goal is documentation, we aim to build a system that can centrally collect, compress and manage the videos. We focus on Dash Camera Videos (DCVs), which are used by people to document the route they drive each day. We proposed a system that can classify videos according to the route they drove using GPS information and visual information. When new DCVs are recorded, their bit-rate can be reduced by jointly compressing them with videos recorded on the similar route. Experimental results show that our system outperforms other similar solutions and the standard HEVC particularly in varying illumination.

The First-Person Video viewing experience topic and the Dashcam Video compression topic are two representations of applications rely on Visual Odometers (VOs): visual augmentation and robotic perception. Different applications have different requirement for VOs. And the performance of VOs are also influenced by many different factors. To help our system and other users that also work on similar applications, we further propose a system that can investigate the performance of different VOs under various factors. The proposed system is shown to be able to provide suggestion on selecting VOs based on the application.