EgoReID: Cross-view Self-Identification and Human Re-identification in Egocentric and Surveillance Videos

TL;DR

This paper introduces a CRF-based method for cross-view human re-identification between egocentric and top-view videos, addressing the challenge of matching individuals across drastically different viewpoints.

Contribution

It presents a novel approach for re-identification across egocentric and top-view videos, a less explored area in human identification research.

Findings

Effective re-identification across egocentric and top-view videos

Demonstrated efficiency over various video datasets

Addresses drastic viewpoint differences in human re-identification

Abstract

Human identification remains to be one of the challenging tasks in computer vision community due to drastic changes in visual features across different viewpoints, lighting conditions, occlusion, etc. Most of the literature has been focused on exploring human re-identification across viewpoints that are not too drastically different in nature. Cameras usually capture oblique or side views of humans, leaving room for a lot of geometric and visual reasoning. Given the recent popularity of egocentric and top-view vision, re-identification across these two drastically different views can now be explored. Having an egocentric and a top view video, our goal is to identify the cameraman in the content of the top-view video, and also re-identify the people visible in the egocentric video, by matching them to the identities present in the top-view video. We propose a CRF-based method to address the two problems. Our experimental results demonstrates the efficiency of the proposed approach over a variety of video recorded from two views.