Co-interest Person Detection from Multiple Wearable Camera Videos
This solves the challenge of person detection in multi-view wearable camera videos for applications like surveillance or social interaction analysis, but it is incremental as it builds on existing motion pattern and CRF techniques.
The paper addresses the problem of identifying the co-interest person (CIP) who draws attention from multiple wearable camera wearers in synchronized videos, achieving effective detection even when people have similar appearances.
Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views. In this paper, we tackle a new problem of locating the co-interest person (CIP), i.e., the one who draws attention from most camera wearers, from temporally synchronized videos taken by multiple wearable cameras. Our basic idea is to exploit the motion patterns of people and use them to correlate the persons across different videos, instead of performing appearance-based matching as in traditional video co-segmentation/localization. This way, we can identify CIP even if a group of people with similar appearance are present in the view. More specifically, we detect a set of persons on each frame as the candidates of the CIP and then build a Conditional Random Field (CRF) model to select the one with consistent motion patterns in different videos and high spacial-temporal consistency in each video. We collect three sets of wearable-camera videos for testing the proposed algorithm. All the involved people have similar appearances in the collected videos and the experiments demonstrate the effectiveness of the proposed algorithm.