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Feature-Based Tracking via SURF Detector and BRISK Descriptor

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Book cover Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 810))

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Abstract

Marker-less tracking has become vital for a variety of vision-based tasks because it tracks some natural regions of the object rather than using fiducial markers. In marker-less feature-based tracking salient regions in the images are identified by the detector and information about these regions are extracted and stored by the descriptor for matching. Speeded-Up Robust Feature (SURF) is considered as the most robust detector and descriptor so far. SURF detects the feature points that are unique and repeatable. It uses integral images which provide a base for the low computational expense. However, descriptors generation and matching for SURF is a time-consuming task. Binary Robust Invariant Scalable Key-points (BRISK) is a scale and rotation invariant binary descriptor. It reduces the computational cost due to its binary nature. This paper presents a marker-less tracking system that tracks natural features of the object in real-time and is very economical in terms of computation. The proposed system is based on SURF detector, as it identifies highly repeatable interest points in the object and BRISK descriptor, due to its low computational cost and invariance to scale and rotation which is vital for every visual tracking system.

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Authors and Affiliations

  1. University of Malakand, Lower Dir, KPK, Pakistan

    Sangeen Khan & Sehat Ullah

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  1. Sangeen Khan
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  2. Sehat Ullah
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Correspondence to Sangeen Khan .

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Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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Khan, S., Ullah, S. (2020). Feature-Based Tracking via SURF Detector and BRISK Descriptor. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_15

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