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Dense descriptor for visual tracking and robust update model strategy

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Abstract

Context analysis is a research field that is attracting growing interest in recent years, especially due to the encouraging results carried out by the semantic-based approach. Anyway, semantic strategies entail the use of trackers capable to show robustness to long-term occlusions, viewpoint changes and identity swap that represent the main problem of many tracking-by-detection solutions. This paper proposes a robust tracking-by-detection framework based on dense SIFT descriptors in combination with an ad-hoc target appearance model update able to overtake the discussed issues. The obtained performances show how our tracker competes with state-of-the-art results and manages occlusions, clutter, changes of scale, rotation and appearance, better than competing tracking methods.

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Authors and Affiliations

  1. National Research Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems (ISASI), C/O DHITECH Campus Universitario - Via Monteroni sn, Lecce, 73100, Italy

    Pier Luigi Mazzeo, Paolo Spagnolo, Marco Leo, Pierluigi Carcagnì, Marco Del Coco & Cosimo Distante

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  1. Pier Luigi Mazzeo
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  2. Paolo Spagnolo
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  3. Marco Leo
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  4. Pierluigi Carcagnì
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  6. Cosimo Distante
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Correspondence to Pier Luigi Mazzeo.

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Mazzeo, P.L., Spagnolo, P., Leo, M. et al. Dense descriptor for visual tracking and robust update model strategy. J Ambient Intell Human Comput 11, 3089–3099 (2020). https://doi.org/10.1007/s12652-017-0461-0

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  • DOI: https://doi.org/10.1007/s12652-017-0461-0

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