Animal behavior classification using DeepLabCut

Shiori Fujimori; Takaaki Ishikawa; Hiroshi Watanabe

doi:10.1109/GCCE50665.2020.9291715

Animal behavior classification using DeepLabCut

Shiori Fujimori, Takaaki Ishikawa, Hiroshi Watanabe

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

In this paper, we introduce a method to classify animal behaviors from videos taken by a fixed-point camera. In order to classify animal behavior, it is necessary to detect and track the animals. Conventional approaches for detecting moving objects are based on background subtraction and frame subtraction. Conventional methods are not suitable for detection of animals kept indoors since they are susceptible to sunlight and shadow. We propose a method to track animals and classify their behavior using skeletal information obtained by DeepLabCut. The experimental results show that the proposed method is superior to the conventional method.

Original language	English
Title of host publication	2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	254-257
Number of pages	4
ISBN (Electronic)	9781728198026
DOIs	https://doi.org/10.1109/GCCE50665.2020.9291715
Publication status	Published - 2020 Oct 13
Event	9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan Duration: 2020 Oct 13 → 2020 Oct 16

Publication series

Name	2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference	9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Country/Territory	Japan
City	Kobe
Period	20/10/13 → 20/10/16

Keywords

DeepLabCut
animal behavior
deep learning

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Media Technology
Instrumentation
Computer Networks and Communications
Computer Vision and Pattern Recognition

Access to Document

10.1109/GCCE50665.2020.9291715

Cite this

Animal behavior classification using DeepLabCut. / Fujimori, Shiori; Ishikawa, Takaaki; Watanabe, Hiroshi.

2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 254-257 9291715 (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fujimori, S, Ishikawa, T & Watanabe, H 2020, Animal behavior classification using DeepLabCut. in 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020., 9291715, 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 254-257, 9th IEEE Global Conference on Consumer Electronics, GCCE 2020, Kobe, Japan, 20/10/13. https://doi.org/10.1109/GCCE50665.2020.9291715

@inproceedings{f7c1367ee5394999980aadf2028a622a,

title = "Animal behavior classification using DeepLabCut",

abstract = "In this paper, we introduce a method to classify animal behaviors from videos taken by a fixed-point camera. In order to classify animal behavior, it is necessary to detect and track the animals. Conventional approaches for detecting moving objects are based on background subtraction and frame subtraction. Conventional methods are not suitable for detection of animals kept indoors since they are susceptible to sunlight and shadow. We propose a method to track animals and classify their behavior using skeletal information obtained by DeepLabCut. The experimental results show that the proposed method is superior to the conventional method. ",

keywords = "DeepLabCut, animal behavior, deep learning",

author = "Shiori Fujimori and Takaaki Ishikawa and Hiroshi Watanabe",

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AU - Watanabe, Hiroshi

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N2 - In this paper, we introduce a method to classify animal behaviors from videos taken by a fixed-point camera. In order to classify animal behavior, it is necessary to detect and track the animals. Conventional approaches for detecting moving objects are based on background subtraction and frame subtraction. Conventional methods are not suitable for detection of animals kept indoors since they are susceptible to sunlight and shadow. We propose a method to track animals and classify their behavior using skeletal information obtained by DeepLabCut. The experimental results show that the proposed method is superior to the conventional method.

AB - In this paper, we introduce a method to classify animal behaviors from videos taken by a fixed-point camera. In order to classify animal behavior, it is necessary to detect and track the animals. Conventional approaches for detecting moving objects are based on background subtraction and frame subtraction. Conventional methods are not suitable for detection of animals kept indoors since they are susceptible to sunlight and shadow. We propose a method to track animals and classify their behavior using skeletal information obtained by DeepLabCut. The experimental results show that the proposed method is superior to the conventional method.

KW - DeepLabCut

KW - animal behavior

KW - deep learning

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DO - 10.1109/GCCE50665.2020.9291715

M3 - Conference contribution

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T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

SP - 254

EP - 257

BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE Global Conference on Consumer Electronics, GCCE 2020

Y2 - 13 October 2020 through 16 October 2020

ER -

Animal behavior classification using DeepLabCut

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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