Identifying Same Persons from Temporally Synchronized Videos Taken by Multiple Wearable Cameras

Abstract

Video-based human action recognition benefits from multiple cameras which can provide temporally synchronized, multi-view videos. Cross-video person identification, i.e., determining whether at a given time, persons tracked in different videos are the same person or not, is a key step to integrate such multi-view information for collaborative action recognition. For fixed cameras, this step is relatively easy since different cameras can be precalibrated. In this paper, we study cross-video person identification for wearable cameras, which are constantly moving with the wearers. Specifically, we take the tracked persons from different videos to be the same person if their 3D poses are the same, given that these videos are synchronized. We adapt an existing algorithm to estimate the tracked person's 3D poses in each 2D video using motionbased features. Experiments show that, although 3D pose estimation is not perfect, the proposed method can still lead to better cross-video person identification than using appearance information.