A fast multi-scale covariance descriptor for object re-identification

In many surveillance systems, there is a need to determine if a given object (person, group of persons, vehicle, …) has already been observed over a network of cameras. It is the object re-identification problem. Solving this problem involves matching observation of objects across disjoint camera views. Uncalibrated fixed or mobile cameras with non-overlapping field of view generate uncontrolled variation in view point, background and lighting. In such situations, a robust and invariant image description is required. A multi-scale covariance image descriptor and a quadtree based scheme are proposed to describe any object of interest. We describe a fast method for computation of multi-scale covariance descriptor. The descriptor is evaluated in person re-identification application using the VIPeR dataset. We show that the proposed multi-scale approach outperforms existing mono-scale image description methods.

► Object re-identification requires efficient image description. ► A fast quadtree based image description is proposed. ► Multi-scale image features are more effectives than mono-scale ones.