Standalone evaluation of deterministic video tracking

Abstract

We present an approach for performance evaluation of deterministic video trackers without ground-truth data. The proposed approach detects if a tracker is correctly operating over time using two main steps. First, it transforms the output of the localization step into a distribution of the target state, which emulates a multi-hypothesis tracker. Then, the uncertainty of such distribution is estimated to determine the time instants when the tracker is stable. A time-reversed analysis is used to identify tracker recovery after unsuccessful operation. The proposed approach is demonstrated on the well-known MeanShift tracker. The results over a heterogeneous dataset show that the proposed approach outperforms the related state-of-the-art methods in presence of tracking challenges such as occlusions, illumination and scale changes, and clutter.