Boosted human re-identification using Riemannian manifolds
Image and Vision Computing
| Hi-index | 0.00 |
In this paper, we present a new solution to the problem of matching groups of people across multiple non-overlapping 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.