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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

4. Tracking of Domestic Animals in Thermal Videos by Tensor Decompositions

verfasst von : Ivo Draganov, Rumen Mironov

Erschienen in: New Approaches for Multidimensional Signal Processing

Verlag: Springer Singapore

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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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Vorheriges Kapitel Comparative Analysis of the Hierarchical 3D-SVD and Reduced Inverse Tensor Pyramid in Regard to Famous 3D Orthogonal Transforms
Nächstes Kapitel Partial Contour Matching Based on Affine Curvature Scale Space Descriptors
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Metadaten
Titel
Tracking of Domestic Animals in Thermal Videos by Tensor Decompositions
verfasst von
Ivo Draganov
Rumen Mironov
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
New Approaches for Multidimensional Signal Processing

Print ISBN: 978-981-334-675-8

Electronic ISBN: 978-981-334-676-5

Copyright-Jahr: 2021

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

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