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Real-time tracking-with-detection for coping with viewpoint change

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Abstract

We consider real-time visual tracking with targets undergoing viewpoint changes. The problem is evaluated on a new and extensive dataset of vehicles undergoing large viewpoint changes. We propose an evaluation method in which tracking accuracy is measured under real-time computational complexity constraints and find that state-of-the-art agnostic trackers, as well as class detectors, are still struggling with this task. We study tracking schemes fusing real-time agnostic trackers with a non-real-time class detector used for template update, with two dominating update strategies emerging. We rigorously analyze the template update latency and demonstrate that such methods significantly outperform stand-alone trackers and class detectors. Results are demonstrated using two different trackers and a state-of-the-art classifier, and at several operating points of algorithm/hardware computational speed.

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  1. http://www.eng.tau.ac.il/~oron/TWD/TWD.html.

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Authors and Affiliations

  1. Tel Aviv University, Tel Aviv, 69978, Israel

    Shaul Oron & Shai Avidan

  2. Advanced Technical Labs Israel, Microsoft Research, Herzliya, Israel

    Aharon Bar-Hillel

Authors
  1. Shaul Oron
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  2. Aharon Bar-Hillel
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  3. Shai Avidan
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Correspondence to Shaul Oron.

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Oron, S., Bar-Hillel, A. & Avidan, S. Real-time tracking-with-detection for coping with viewpoint change. Machine Vision and Applications 26, 507–518 (2015). https://doi.org/10.1007/s00138-015-0676-z

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  • DOI: https://doi.org/10.1007/s00138-015-0676-z

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