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Discriminative Scale Space Tracking
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2017 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 39, no 8, 1561-1575 p.Article in journal (Refereed) Published
Abstract [en]

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5 percent in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50 percent higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2017. Vol. 39, no 8, 1561-1575 p.
Keyword [en]
Visual tracking; scale estimation; correlation filters
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-139382DOI: 10.1109/TPAMI.2016.2609928ISI: 000404606300006PubMedID: 27654137OAI: oai:DiVA.org:liu-139382DiVA: diva2:1129861
Note

Funding Agencies|Swedish Foundation for Strategic Research; Swedish Research Council; Strategic Vehicle Research and Innovation (FFI); Wallenberg Autonomous Systems Program; National Supercomputer Centre; Nvidia

Available from: 2017-08-07 Created: 2017-08-07 Last updated: 2017-08-07

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