Evaluation of on-line quality estimators for object tracking
Failure of tracking algorithms is inevitable in real and on-line tracking systems. The online estimation of the track quality is therefore desirable for detecting tracking failures while the algorithm is operating. In this paper, we propose a taxonomy and present a comparative evaluation of online quality estimators for video object tracking. The measures are compared over a heterogeneous video dataset with standard sequences. Among other results, the experiments show, that the Observation Likelihood (OL) measure is an appropriate quality measure for overall tracking performance evaluation, while the Template Inverse Matching (TIM) measure is appropriate to detect the start and the end instants of tracking failures.