Evaluating Descriptors Performances for Object Tracking on Natural Video Data
Résumé
In this paper, a new framework is presented for the quantita- tive evaluation of the performance of appearance models composed of an object descriptor and a similarity measure in the context of object track- ing. The evaluation is based on natural videos, and takes advantage of existing ground-truths from object tracking benchmarks. The proposed metrics evaluate the ability of an appearance model to discriminate an object from the clutter. This allows comparing models which may use di- verse kinds of descriptors or similarity measures in a principled manner. The performances measures can be global, but time-oriented performance evaluation is also presented. The insights that the proposed framework can bring on appearance models properties with respect to tracking are illustrated on natural video data.