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Dynamic Objectness for Adaptive Tracking

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7726))

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Abstract

A fundamental problem of object tracking is to adapt to unseen views of the object while not getting distracted by other objects. We introduce Dynamic Objectness in a discriminative tracking framework to sporadically re-discover the tracked object based on motion. In doing so, drifting is effectively limited since tracking becomes more aware of objects as independently moving entities in the scene. The approach not only follows the object, but also the background to not easily adapt to other distracting objects. Finally, an appearance model of the object is incrementally built for an eventual re-detection after a partial or full occlusion. We evaluated it on several well-known tracking sequences and demonstrate results with superior accuracy, especially in difficult sequences with changing aspect ratios, varying scale, partial occlusion and non-rigid objects.

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Author information

Authors and Affiliations

  1. Computer Vision Laboratory, ETH Zurich, Switzerland

    Severin Stalder, Helmut Grabner & Luc Van Gool

  2. ESAT - PSI / IBBT, K.U. Leuven, Belgium

    Luc Van Gool

Authors
  1. Severin Stalder
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  2. Helmut Grabner
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  3. Luc Van Gool
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Stalder, S., Grabner, H., Van Gool, L. (2013). Dynamic Objectness for Adaptive Tracking. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_4

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  • DOI: https://doi.org/10.1007/978-3-642-37431-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37430-2

  • Online ISBN: 978-3-642-37431-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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