Matching Groups of People by Covariance Descriptor

In this paper, we present a new solution to the problem of matching groups of people across multiple nonoverlapping cameras. Similar to the problem of matching individuals across cameras, matching groups of people also faces challenges such as variations of illumination conditions, poses and camera parameters. Moreover, people often swap their positions while walking in a group. In this paper, we propose to use covariance descriptor in appearance matching of group images. Covariance descriptor is shown to be a discriminative descriptor which captures both appearance and statistical properties of image regions. Furthermore, it presents a natural way of combining multiple heterogeneous features together with a relatively low dimensionality. Experimental results on two different datasets demonstrate the effectiveness of the proposed method.

• Type: Journal
• Year: 2010
• Where: ICPR
• Authors: Yinghao Cai, Valtteri Takala, Matti Pietikäinen
• Keywords: Computer Vision, Covariance Descriptor, Discriminative Descriptor, ICPR 2010, Multiple Nonoverlapping Cameras