Preserving Structure in Model-Free Tracking
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...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 36(2014), 4 vom: 01. Apr., Seite 756-69 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2014
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't |
| Zusammenfassung: | 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 |
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| Beschreibung: | Date Completed 27.11.2015 Date Revised 10.09.2015 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2013.221 |