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Preserving Structure in Model-Free Tracking
April 2014 (vol. 36 no. 4)
pp. 756-769
Lu Zhang, Dept. of Intell. Syst., Delft Univ. of Technol., Delft, Netherlands
Laurens J. P. van der Maaten, Dept. of Intell. Syst., Delft Univ. of Technol., Delft, Netherlands
Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation of the object. Whilst the performance of model-free trackers has recently improved significantly, simultaneously tracking multiple objects with similar appearance remains very hard. In this paper, we propose a new multi-object model-free tracker (using a tracking-by-detection framework) that resolves this problem by incorporating spatial constraints between the objects. The spatial constraints are learned along with the object detectors using an online structured SVM algorithm. The experimental evaluation of our structure-preserving object tracker (SPOT) reveals substantial performance improvements in multi-object tracking. We also show that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object. Moreover, we show that SPOT can be used to adapt generic, model-based object detectors during tracking to tailor them towards a specific instance of that object.
Index Terms:
Target tracking,Deformable models,Bismuth,Detectors,Support vector machines,Feature extraction,structured SVM,Model-free tracking,multiple-object tracking,online learning
Citation:
Lu Zhang, Laurens J. P. van der Maaten, "Preserving Structure in Model-Free Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 4, pp. 756-769, April 2014, doi:10.1109/TPAMI.2013.221
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