Adaptive online performance evaluation of video trackers.

We propose an adaptive framework to estimate the quality of video tracking algorithms without ground-truth data. The framework is divided into two main stages, namely, the estimation of the tracker condition to identify temporal segments during which a target is lost and the measurement of the quality of the estimated track when the tracker is successful. A key novelty of the proposed framework is the capability of evaluating video trackers with multiple failures and recoveries over long sequences. Successful tracking is identified by analyzing the uncertainty of the tracker, whereas track recovery from errors is determined based on the time-reversibility constraint. The proposed approach is demonstrated on a particle filter tracker over a heterogeneous data set. Experimental results show the effectiveness and robustness of the proposed framework that improves state-of-the-art approaches in the presence of tracking challenges such as occlusions, illumination changes, and clutter and on sequences containing multiple tracking errors and recoveries.