Person re-identification (re-id) aims to match people across non-overlapping
camera views. So far the RGB-based appearance is widely used in most existing
works. However, when people appeared in extreme illumination or changed
clothes, the RGB appearance-based re-id methods tended to fail. To overcome
this problem, we propose to exploit depth information to provide more invariant
body shape and skeleton information regardless of illumination and color
change. More specifically, we exploit depth voxel covariance descriptor and
further propose a locally rotation invariant depth shape descriptor called
Eigen-depth feature to describe pedestrian body shape. We prove that the
distance between any two covariance matrices on the Riemannian manifold is
equivalent to the Euclidean distance between the corresponding Eigen-depth
features. Furthermore, we propose a kernelized implicit feature transfer scheme
to estimate Eigen-depth feature implicitly from RGB image when depth
information is not available. We find that combining the estimated depth
features with RGB-based appearance features can sometimes help to better reduce
visual ambiguities of appearance features caused by illumination and similar
clothes. The effectiveness of our models was validated on publicly available
depth pedestrian datasets as compared to related methods for person
re-identification.

该研究提出了一种基于深度信息的人物重识别方法，利用深度数据提取人体骨架和形状特征，进而提出了一种具有旋转不变性的 Eigen-depth 特征描述符，并通过基于核技术的隐式特征转换方法在缺少深度数据时估算特征。实验结果表明，该方法可以在改善外观特征因光照和相似服饰引起的视觉混淆问题上取得较好的效果。