Evaluation of tracking algorithm performance without ground-truth data

Visual tracking is a topic on which a lot of scientific work has been carried out in the last years. An important aspect of tracking algorithms is the performance evaluation, which has been carried out typically through hand-labeled ground-truth data. Since the manual generation of ground truth is a time-consuming, error-prone and tedious task, recently many researchers have focused their attention on self-evaluation techniques for performance analysis. In this paper we propose a novel tool that enables image processing researchers to test the performance of tracking algorithms without resorting to hand-labeled ground truth data. The proposed approach consists of computing a set of features describing shape, appearance and motion of the tracked objects and combining them through a naive Bayesian classifier, in order to obtain a probability score representing the overall evaluation of each tracking decision. The method was tested on three different targets (vehicles, humans and fish) with three different tracking algorithms and the results show how this approach is able to reflect the quality of the performed tracking.