The Experts below are selected from a list of 6444 Experts worldwide ranked by ideXlab platform
Tilo Burghardt - One of the best experts on this subject based on the ideXlab platform.
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Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: William Andrew, Neill W Campbell, Jing Gao, Andrew W. Dowsey, Tilo BurghardtAbstract:Holstein-Friesian Cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques. Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training. We propose the use of SoftMax-based reciprocal triplet loss to address the identification problem and evaluate the techniques in detail against fixed herd paradigms. We find that deep metric learning systems show strong performance even when many Cattle unseen during system training are to be identified and re-identified - achieving 98.2% accuracy when trained on just half of the population. This work paves the way for facilitating the non-intrusive monitoring of Cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary research such as behavioural analysis, disease outbreak tracing, and more. Key parts of the source code, network weights and underpinning datasets are available publicly.
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Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle (Extended Abstract)
2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This work covers the robotic drone integration of a re-identification system for Friesian Cattle. We have built a computationally-enhanced M100 UAV platform with an on-board deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. For autonomous drone-based identification we describe an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream Convolutional Neural Network (CNN) delivering exploratory agency and an InceptionV3-based biometric Long-term Recurrent Convoluational Network (LRCN) for individual animal identification. We evaluate the performance of components offline, and also online via real-world field tests of autonomous low-altitude flight in a farm environment. The presented proof-of-concept system is a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments and inside farms for tag-less AI support in farming and ecology. The work is published in full in IROS 2019 [4].
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WACV Workshops - Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle (Extended Abstract)
2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This work covers the robotic drone integration of a re-identification system for Friesian Cattle. We have built a computationally-enhanced M100 UAV platform with an on-board deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. For autonomous drone-based identification we describe an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream Convolutional Neural Network (CNN) delivering exploratory agency and an InceptionV3-based biometric Long-term Recurrent Convoluational Network (LRCN) for individual animal identification. We evaluate the performance of components offline, and also online via real-world field tests of autonomous low-altitude flight in a farm environment. The presented proof-of-concept system is a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments and inside farms for tag-less AI support in farming and ecology. The work is published in full in IROS 2019 [4].
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
arXiv: Robotics, 2019Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream CNN delivering exploratory agency and an InceptionV3-based biometric LRCN for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 146.7 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind and a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
2019 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2019Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation. The system is able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft: (1) a YOLOv2-based species detector, (2) a dual-stream deep network delivering exploratory agency, and (3) an InceptionV3-based biometric long-term recurrent convolutional network for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 147 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind. It represents a practical step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
William Andrew - One of the best experts on this subject based on the ideXlab platform.
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Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: William Andrew, Neill W Campbell, Jing Gao, Andrew W. Dowsey, Tilo BurghardtAbstract:Holstein-Friesian Cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques. Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training. We propose the use of SoftMax-based reciprocal triplet loss to address the identification problem and evaluate the techniques in detail against fixed herd paradigms. We find that deep metric learning systems show strong performance even when many Cattle unseen during system training are to be identified and re-identified - achieving 98.2% accuracy when trained on just half of the population. This work paves the way for facilitating the non-intrusive monitoring of Cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary research such as behavioural analysis, disease outbreak tracing, and more. Key parts of the source code, network weights and underpinning datasets are available publicly.
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Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle (Extended Abstract)
2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This work covers the robotic drone integration of a re-identification system for Friesian Cattle. We have built a computationally-enhanced M100 UAV platform with an on-board deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. For autonomous drone-based identification we describe an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream Convolutional Neural Network (CNN) delivering exploratory agency and an InceptionV3-based biometric Long-term Recurrent Convoluational Network (LRCN) for individual animal identification. We evaluate the performance of components offline, and also online via real-world field tests of autonomous low-altitude flight in a farm environment. The presented proof-of-concept system is a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments and inside farms for tag-less AI support in farming and ecology. The work is published in full in IROS 2019 [4].
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WACV Workshops - Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle (Extended Abstract)
2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This work covers the robotic drone integration of a re-identification system for Friesian Cattle. We have built a computationally-enhanced M100 UAV platform with an on-board deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. For autonomous drone-based identification we describe an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream Convolutional Neural Network (CNN) delivering exploratory agency and an InceptionV3-based biometric Long-term Recurrent Convoluational Network (LRCN) for individual animal identification. We evaluate the performance of components offline, and also online via real-world field tests of autonomous low-altitude flight in a farm environment. The presented proof-of-concept system is a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments and inside farms for tag-less AI support in farming and ecology. The work is published in full in IROS 2019 [4].
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
arXiv: Robotics, 2019Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream CNN delivering exploratory agency and an InceptionV3-based biometric LRCN for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 146.7 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind and a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
2019 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2019Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation. The system is able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft: (1) a YOLOv2-based species detector, (2) a dual-stream deep network delivering exploratory agency, and (3) an InceptionV3-based biometric long-term recurrent convolutional network for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 147 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind. It represents a practical step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
Colin Greatwood - One of the best experts on this subject based on the ideXlab platform.
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Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle (Extended Abstract)
2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This work covers the robotic drone integration of a re-identification system for Friesian Cattle. We have built a computationally-enhanced M100 UAV platform with an on-board deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. For autonomous drone-based identification we describe an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream Convolutional Neural Network (CNN) delivering exploratory agency and an InceptionV3-based biometric Long-term Recurrent Convoluational Network (LRCN) for individual animal identification. We evaluate the performance of components offline, and also online via real-world field tests of autonomous low-altitude flight in a farm environment. The presented proof-of-concept system is a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments and inside farms for tag-less AI support in farming and ecology. The work is published in full in IROS 2019 [4].
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WACV Workshops - Fusing Animal Biometrics with Autonomous Robotics: Drone-based Search and Individual ID of Friesian Cattle (Extended Abstract)
2020 IEEE Winter Applications of Computer Vision Workshops (WACVW), 2020Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This work covers the robotic drone integration of a re-identification system for Friesian Cattle. We have built a computationally-enhanced M100 UAV platform with an on-board deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. For autonomous drone-based identification we describe an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream Convolutional Neural Network (CNN) delivering exploratory agency and an InceptionV3-based biometric Long-term Recurrent Convoluational Network (LRCN) for individual animal identification. We evaluate the performance of components offline, and also online via real-world field tests of autonomous low-altitude flight in a farm environment. The presented proof-of-concept system is a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments and inside farms for tag-less AI support in farming and ecology. The work is published in full in IROS 2019 [4].
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
arXiv: Robotics, 2019Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream CNN delivering exploratory agency and an InceptionV3-based biometric LRCN for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 146.7 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind and a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
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Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
2019 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2019Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation. The system is able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft: (1) a YOLOv2-based species detector, (2) a dual-stream deep network delivering exploratory agency, and (3) an InceptionV3-based biometric long-term recurrent convolutional network for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 147 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind. It represents a practical step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
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IROS - Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference
2019 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2019Co-Authors: William Andrew, Colin Greatwood, Tilo BurghardtAbstract:This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation. The system is able to autonomously find and visually identify by coat pattern individual Holstein Friesian Cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft: (1) a YOLOv2-based species detector, (2) a dual-stream deep network delivering exploratory agency, and (3) an InceptionV3-based biometric long-term recurrent convolutional network for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 147 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind. It represents a practical step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
M. D. Royal - One of the best experts on this subject based on the ideXlab platform.
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Short communication: Genetic and nongenetic factors influencing Ostertagia ostertagi antibodies in UK Holstein-Friesian Cattle.
Journal of Dairy Science, 2010Co-Authors: Caroline Hayhurst, K. Hunter, Andrew J Bradley, A. B. Forbes, M. D. RoyalAbstract:Abstract The purpose of this study was to estimate and discuss the genetic variation, heritability, and effects of nongenetic factors on the ability of Holstein-Friesian cows to produce an immune response by producing IgG antibodies to Ostertagia ostertagi . Total IgG (IgG 1 and IgG 2 ) antibody levels were determined using an ELISA and measured using optical density ratio (ODR=OD sample – OD negative control /OD positive control – OD negative control ) from milk samples collected from 1,276 Holstein-Friesian Cattle in 229 commercial dairy farms from 2002 to 2004 during their first (82%) and other (2 to 12) lactations. A sire (n=461) model was fitted to the ODR data using ASREML software, and variance components were estimated. The ability to produce O. ostertagi antibodies as measured by ODR had a heritability of 0.13±0.12, and both season of sample and herd had a significant effect on total IgG levels. To conclude, this study has ascertained that genetic variation is present in the ability of dairy cows to mount an immune response to the parasite O. ostertagi . Inasmuch as evidence exists that IgG is linked to protective immunity against the parasite via a reduction in its reproductive ability, this trait may be of potential interest to genetic selection programs as an aid to reduce the effect of O. ostertagi in dairy herds.
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Short communication: Genetic and nongenetic factors influencing Ostertagia ostertagi antibodies in UK Holstein-Friesian Cattle.
Journal of Dairy Science, 2010Co-Authors: Caroline Hayhurst, K. Hunter, Andrew J Bradley, A. B. Forbes, M. D. RoyalAbstract:Abstract The purpose of this study was to estimate and discuss the genetic variation, heritability, and effects of nongenetic factors on the ability of Holstein-Friesian cows to produce an immune response by producing IgG antibodies to Ostertagia ostertagi . Total IgG (IgG 1 and IgG 2 ) antibody levels were determined using an ELISA and measured using optical density ratio (ODR=OD sample – OD negative control /OD positive control – OD negative control ) from milk samples collected from 1,276 Holstein-Friesian Cattle in 229 commercial dairy farms from 2002 to 2004 during their first (82%) and other (2 to 12) lactations. A sire (n=461) model was fitted to the ODR data using ASREML software, and variance components were estimated. The ability to produce O. ostertagi antibodies as measured by ODR had a heritability of 0.13±0.12, and both season of sample and herd had a significant effect on total IgG levels. To conclude, this study has ascertained that genetic variation is present in the ability of dairy cows to mount an immune response to the parasite O. ostertagi . Inasmuch as evidence exists that IgG is linked to protective immunity against the parasite via a reduction in its reproductive ability, this trait may be of potential interest to genetic selection programs as an aid to reduce the effect of O. ostertagi in dairy herds.
Miguel A. Toro - One of the best experts on this subject based on the ideXlab platform.
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Bayesian analysis of lactation curves of Holstein-Friesian Cattle using a nonlinear model.
Journal of Dairy Science, 2000Co-Authors: Romdhane Rekaya, María J. Carabaño, Miguel A. ToroAbstract:A Bayesian procedure was developed for fitting Wood's incomplete Gamma function to test-day milk records of Spanish Holstein Friesian Cattle. Each parameter of Wood's function was considered as a dependent variable in a submodel that accounted for systematic effects and genetic relationships among animals. Marginal posterior distributions of model parameters were obtained using Gibbs sampling. Variables of economic interest, such as 305-d yield, persistency, peak yield, and days in milk at peak day were predicted as functions of Wood's function curve parameters. Heritability estimates were 0.26, 0.32, and 0.19 for parameters of Wood's function and 0.26, 0.14, 0.26, and 0.05 for 305-d yield, persistency, peak yield, and days in milk at peak yield. These estimates indicate that it is possible to modify the shape of the lactation curve through genetic selection. Genetic correlations between parameters of Wood's curve and the aforementioned functions of these parameters suggest that selection for 305-d milk yield would result in higher and later peak yield, but only a slight improvement in persistency is expected.
