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Tracking of Domestic Animals in Thermal Videos by Tensor Decompositions

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New Approaches for Multidimensional Signal Processing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 216))

Abstract

In this paper, we present a comparative analysis of the performance of the Tucker-ALS, CP-ALS, Tucker-ADAL, and the HoRPCA-S tensor decomposition algorithms, applied for tracking of domestic animals in video. Decomposition and full processing time, detection rate, precision, and F-measure are the evaluating parameters revealing the efficiency of each algorithm. Promising results suggest the applicability of the investigated decompositions but also demonstrate particular differences among them in terms of decomposition time and detection rate. In order to increase the detection rate of systems of parallel type employing multiple decomposition algorithms we propose a score fusion with fair voting which performs better than some of the tested algorithms alone.

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Acknowledgements

This work was supported by the National Science Fund of Bulgaria: KP-06-H27/16 Development of efficient methods and algorithms for tensor-based processing and analysis of multidimensional images with application in interdisciplinary areas.

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Authors and Affiliations

  1. Technical University of Sofia, 8 Kliment Ohridski Blvd, Sofia, Bulgaria

    Ivo Draganov & Rumen Mironov

Authors
  1. Ivo Draganov
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  2. Rumen Mironov
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Corresponding author

Correspondence to Ivo Draganov .

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Editors and Affiliations

  1. Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

  2. Technical University of Sofia, Sofia, Bulgaria

    Rumen Mironov

  3. Hunan University of Technology, Changsha, China

    Shengqing Li

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Draganov, I., Mironov, R. (2021). Tracking of Domestic Animals in Thermal Videos by Tensor Decompositions. In: Kountchev, R., Mironov, R., Li, S. (eds) New Approaches for Multidimensional Signal Processing. Smart Innovation, Systems and Technologies, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-33-4676-5_4

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  • DOI: https://doi.org/10.1007/978-981-33-4676-5_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4675-8

  • Online ISBN: 978-981-33-4676-5

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