Symbiotic black-box tracker

Authors:
Longfei Zhang;Yue Gao;Alexander Hauptmann;Rongrong Ji;Gangyi Ding;Boaz Super
Affiliations:
School of Software, Beijing Institute of Technology, Beijing, China;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Department of Electronic Engineering, Columbia University, New York, NY;School of Software, Beijing Institute of Technology, Beijing, China;Motorola Senior Research, Schaumburg, IL
Venue:
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Year:
2012

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Abstract

Many trackers have been proposed for tracking objects individually in previous research. However, it is still difficult to trust any single tracker over a variety of circumstances. Therefore, it is important to estimate how well each tracker performs and fusion the tracking results. In this paper, we propose a symbiotic black-box tracker (SBB) that learns only from the output of individual trackers, which run in parallel, without any detailed information about these trackers and selects the best one to generate the tracking result. All trackers are considered as black-boxes and SBB learns the best combination scheme for all existing tracking results. SBB estimates confidence scores of these trackers. The confidence score is estimated based on the tracking performance of each tracker and the consistency performance among different trackers. SBB is employed to select the best tracker with the maximum confidence score. Experiments and comparisons conducted on the "Caremedia" dataset and the "Caviar" dataset demonstrate the effectiveness of the proposed method.