Robust tracking of people in crowds with covariance descriptors

In order to control riots in crowds, it is helpful to get the ringleader under control. A great support to achieve this task is the capability to automatically track individual persons in a video sequence taken from a crowd. In this paper we address the robustness of such a tracking function. We start from the results of a previous evaluation of tracking methods, where a so-called Covariance-Tracker was found to be most appropriate. This tracker uses covariance matrices as object descriptors, as proposed by Porikli et al. The set of all covariance matrices describes a Riemannian manifold that is used to compare and update the covariance descriptors during tracking. We propose Covariance-Tracker adaptations to improve its performance. Furthermore, we summarize the performance evaluation results of the original method and compare these with the results of the adapted one. The result is a robust method for tracking people in crowds which can improve situational awareness.