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A Superior Tracking Approach: Building a Strong Tracker through Fusion*
Christian Bailer1, Alain Pagani1, and Didier Stricker1, 2
1German Research Center for Artificial Intelligence, Kaiserslautern, Germany
Christian.Bailer@dfki.de
Alain.Pagani@dfki.de
Didier.Stricker@dfki.de
2University of Kaiserslautern, Germany
Abstract. General object tracking is a challenging problem, where each tracking algorithm performs well on different sequences. This is because each of them has different strengths and weaknesses. We show that this fact can be utilized to create a fusion approach that clearly outperforms the best tracking algorithms in tracking performance. Thanks to dynamic programming based trajectory optimization we cannot only outperform tracking algorithms in accuracy but also in other important aspects like trajectory continuity and smoothness. Our fusion approach is very generic as it only requires frame-based tracking results in form of the object’s bounding box as input and thus can work with arbitrary tracking algorithms. It is also suited for live tracking. We evaluated our approach using 29 different algorithms on 51 sequences and show the superiority of our approach compared to state-of-the-art tracking methods.
Keywords: Object Tracking, Data Fusion
Electronic Supplementary Material:
LNCS 8695, p. 170 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014