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Computer Vision Based Analysis of Animal Behavior

Palmér, Tobias LU (2017)
Abstract (Swedish)
The behavior of animals is commonly studied in medicine and biology. There is a large variation in what animals are studied, in experimental paradigms and purpose.
However, many studies on animal behavior have at least one thing in common - it typically involves measuring or studying the kinematics of the animal. To allow for verifiable and quantitative behavioral analysis, the experiments are recorded and kinematic data is extracted from the videos using computer vision methods. This thesis deals with the development of methods that takes recorded videos as input and provides behavioral data as output. The system of methods can be split into three parts - tracking of animal pose, extraction of kinematic features and analysis of the... (More)
The behavior of animals is commonly studied in medicine and biology. There is a large variation in what animals are studied, in experimental paradigms and purpose.
However, many studies on animal behavior have at least one thing in common - it typically involves measuring or studying the kinematics of the animal. To allow for verifiable and quantitative behavioral analysis, the experiments are recorded and kinematic data is extracted from the videos using computer vision methods. This thesis deals with the development of methods that takes recorded videos as input and provides behavioral data as output. The system of methods can be split into three parts - tracking of animal pose, extraction of kinematic features and analysis of the features. This thesis focus mainly on the two first parts. However, an important aspect in the design of the system is that all parts should be compatible. Therefore, all method development has been conducted in collaboration with medical/biological scientists.

This thesis contains computer vision methods for tracking rats, marmosets, zebrafish, jellyfish and zooplankton.
Most of the projects are represented by a scientific paper that outlines the computer vision methods, and a paper that focus on the medical/biological application of the computer vision based system.
One of the methods is applied to study the correlation between fine-kinematic behavior and neuronal activity in rats. Another method is used to characterize the long term effects of the marmoset model of Parkinson's disease. Thirdly, a high-throughput system is developed to quantify drug-induced changes in zebrafish larvae behavior. Fourthly, steps are taken towards a system that allows for studying the correlation between visual stimuli and movement output in the box jellyfish. Lastly, nonlinear positioning methods are proposed for the purpose of studying e.g. multiple threat response in zooplankton inside an aquarium.

Additionally, and seemingly an outlier, this thesis features a novel method for estimating relative camera motion in an underwater setting. The method is not applied for analyzing animal behavior, but it is related to the geometrical problems of refraction encountered while positioning the zooplankton.
In this project, we leverage the pseudo-depth information that is contained in underwater images to design a three point relative pose algorithm.
(Less)
Abstract
The behavior of animals is commonly studied in medicine and biology. There is a large variation in what animals are studied, in experimental paradigms and purpose.
However, many studies on animal behavior have at least one thing in common - it typically involves measuring or studying the kinematics of the animal. To allow for verifiable and quantitative behavioral analysis, the experiments are recorded and kinematic data is extracted from the videos using computer vision methods. This thesis deals with the development of methods that takes recorded videos as input and provides behavioral data as output. The system of methods can be split into three parts - tracking of animal pose, extraction of kinematic features and analysis of the... (More)
The behavior of animals is commonly studied in medicine and biology. There is a large variation in what animals are studied, in experimental paradigms and purpose.
However, many studies on animal behavior have at least one thing in common - it typically involves measuring or studying the kinematics of the animal. To allow for verifiable and quantitative behavioral analysis, the experiments are recorded and kinematic data is extracted from the videos using computer vision methods. This thesis deals with the development of methods that takes recorded videos as input and provides behavioral data as output. The system of methods can be split into three parts - tracking of animal pose, extraction of kinematic features and analysis of the features. This thesis focus mainly on the two first parts. However, an important aspect in the design of the system is that all parts should be compatible. Therefore, all method development has been conducted in collaboration with medical/biological scientists.

This thesis contains computer vision methods for tracking rats, marmosets, zebrafish, jellyfish and zooplankton.
Most of the projects are represented by a scientific paper that outlines the computer vision methods, and a paper that focus on the medical/biological application of the computer vision based system.
One of the methods is applied to study the correlation between fine-kinematic behavior and neuronal activity in rats. Another method is used to characterize the long term effects of the marmoset model of Parkinson's disease. Thirdly, a high-throughput system is developed to quantify drug-induced changes in zebrafish larvae behavior. Fourthly, steps are taken towards a system that allows for studying the correlation between visual stimuli and movement output in the box jellyfish. Lastly, nonlinear positioning methods are proposed for the purpose of studying e.g. multiple threat response in zooplankton inside an aquarium.

Additionally, and seemingly an outlier, this thesis features a novel method for estimating relative camera motion in an underwater setting. The method is not applied for analyzing animal behavior, but it is related to the geometrical problems of refraction encountered while positioning the zooplankton.
In this project, we leverage the pseudo-depth information that is contained in underwater images to design a three point relative pose algorithm. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Professor Fischer, Robert, University of Edinburgh, UK
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Datorseende, bildanalys, djurbeteende, neurofysiologi, tillämpningar, refraktion, Computer vision, animal tracking, behavioral analysis, refractions, underwater images, relative pose
edition
1
pages
299 pages
publisher
Faculty of Engineering, Centre for Mathematical Sciences, Mathematical Statistics, Lund University
defense location
Lecture hall MA01, Matematikhusets Annex, Sölvegatan 18, Lund University, Faculty of Engineering LTH, Lund
defense date
2017-03-08 10:15
ISBN
978-91-7753-168-5
978-91-7753-167-8
language
English
LU publication?
yes
id
52270ab3-e7c4-4eee-b21b-4297e374c178
date added to LUP
2017-02-09 07:53:10
date last changed
2017-02-10 11:21:48
@phdthesis{52270ab3-e7c4-4eee-b21b-4297e374c178,
  abstract     = {The behavior of animals is commonly studied in medicine and biology. There is a large variation in what animals are studied, in experimental paradigms and purpose.<br/>However, many studies on animal behavior have at least one thing in common - it typically involves measuring or studying the kinematics of the animal. To allow for verifiable and quantitative behavioral analysis, the experiments are recorded and kinematic data is extracted from the videos using computer vision methods. This thesis deals with the development of methods that takes recorded videos as input and provides behavioral data as output. The system of methods can be split into three parts - tracking of animal pose, extraction of kinematic features and analysis of the features. This thesis focus mainly on the two first parts. However, an important aspect in the design of the system is that <i>all</i> parts should be compatible. Therefore, all method development has been conducted in collaboration with medical/biological scientists.<br/><br/>This thesis contains computer vision methods for tracking rats, marmosets, zebrafish, jellyfish and zooplankton.<br/>Most of the projects are represented by a scientific paper that outlines the computer vision methods, and a paper that focus on the medical/biological application of the computer vision based system.<br/>One of the methods is applied to study the correlation between fine-kinematic behavior and neuronal activity in rats. Another method is used to characterize the long term effects of the marmoset model of Parkinson's disease. Thirdly, a high-throughput system is developed to quantify drug-induced changes in zebrafish larvae behavior. Fourthly, steps are taken towards a system that allows for studying the correlation between visual stimuli and movement output in the box jellyfish. Lastly, nonlinear positioning methods are proposed for the purpose of studying e.g. multiple threat response in zooplankton inside an aquarium.<br/><br/>Additionally, and seemingly an outlier, this thesis features a novel method for estimating relative camera motion in an underwater setting. The method is not applied for analyzing animal behavior, but it is related to the geometrical problems of refraction encountered while positioning the zooplankton.<br/>In this project, we leverage the pseudo-depth information that is contained in underwater images to design a three point relative pose algorithm.},
  author       = {Palmér, Tobias},
  isbn         = {978-91-7753-168-5},
  keyword      = {Datorseende,bildanalys,djurbeteende,neurofysiologi,tillämpningar,refraktion,Computer vision,animal tracking,behavioral analysis,refractions,underwater images,relative pose},
  language     = {eng},
  month        = {02},
  pages        = {299},
  publisher    = {Faculty of Engineering, Centre for Mathematical Sciences, Mathematical Statistics, Lund University},
  school       = {Lund University},
  title        = {Computer Vision Based Analysis of Animal Behavior},
  year         = {2017},
}