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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

A Bottom-Up Approach for Pig Skeleton Extraction Using RGB Data

verfasst von : Akif Quddus Khan, Salman Khan, Mohib Ullah, Faouzi Alaya Cheikh

Erschienen in: Image and Signal Processing

Verlag: Springer International Publishing

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Abstract

Animal behavior analysis is a crucial task for the industrial farming. In an indoor farm setting, extracting Key joints of animals is essential for tracking the animal for a longer period of time. In this paper, we proposed a deep network that exploits transfer learning to train the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on an hourglass stacked dense-net. In order to train the network, keyframes are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.

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Vorheriges Kapitel A Spectral Hazy Image Database
Nächstes Kapitel Deep Transfer Learning Models for Tomato Disease Detection
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Metadaten
Titel
A Bottom-Up Approach for Pig Skeleton Extraction Using RGB Data
verfasst von
Akif Quddus Khan
Salman Khan
Mohib Ullah
Faouzi Alaya Cheikh
Copyright-Jahr
2020
Buch
Image and Signal Processing

Print ISBN: 978-3-030-51934-6

Electronic ISBN: 978-3-030-51935-3

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-51935-3_6

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