Online Evaluation of Tracking Algorithm Performance

This paper presents a method to evaluate online the performance of tracking algorithms in surveillance videos. We use a set of features to compute the confidence of trajectories and also the precision of tracking results. A global score is computed online based on these features and is used to estimate the performance of tracking algorithms. The method has been tested with two real video sequences and two tracking algorithms. The similar variations between the results obtained by the proposed method and the output of a supervised evaluation tool using ground truth data have showed the performance of our global score. The advantages of our approach over the existing state of the art approaches are: (i) few a priori knowledge information is required, (ii) the method can be applied in complex scenes containing several mobile objects and (iii) we can simultaneously compare the performance of different tracking algorithms.