Equine Welfare Assessment: Horse Motion Evaluation and Comparison to Manual Pain Measurements

Rueß, Dominik; Rueß, Jochen; Hümmer, Christian; Deckers, Niklas; Migal, Vitaliy; Kienapfel, Kathrin; Wieckert, Anne; Barnewitz, Dirk; Reulke, Ralf

doi:10.1007/978-3-030-34879-3_13

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Table 3 dissects the results and evaluation protocols of the works surveyed here. However, we have excluded works which do not involve a down-stream classification task, but rather describe pain behavior using computer vision (e.g., Reuss et al [125] and Statham et al [91]), as well as pre-prints, and the three works which address only AU classification. The categories included in Table 3 are explained as follows.…”

Section: Performance Evaluationmentioning

confidence: 99%

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

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Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of affective states and pain in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic -classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

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Section: Performance Evaluationmentioning

confidence: 99%

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Table 3 dissects the results and evaluation protocols of the works surveyed here. However, we have excluded works which do not involve a down-stream classification task, but rather describe pain behavior using computer vision (e.g., Rueß et al, 2019 andStatham et al, 2020), as well as pre-prints, and the three works which address only AU classification. The categories included in Table 3 are explained as follows.…”

Section: Performance Evaluationmentioning

confidence: 99%

Going Deeper than Tracking: A Survey of Computer-Vision Based Recognition of Animal Pain and Emotions

et al. 2022

View full text Add to dashboard Cite

Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go ‘deeper’ than tracking, and address automated recognition of animals’ internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of pain and emotional states in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic—classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

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“…Therefore it is of significant importance to investigate and develop an automatic, objective, accurate, and quantifiable measurement system for equine behaviors. Such a system will allow caretakers to identify variations in the animal behavioral repertoire in real-time, decreasing the workloads in veterinary clinics and improving the husbandry and management of animals [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data

Mao

Huang

Gan

et al. 2021

Sensors

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With the recent advances in deep learning, wearable sensors have increasingly been used in automated animal activity recognition. However, there are two major challenges in improving recognition performance—multi-modal feature fusion and imbalanced data modeling. In this study, to improve classification performance for equine activities while tackling these two challenges, we developed a cross-modality interaction network (CMI-Net) involving a dual convolution neural network architecture and a cross-modality interaction module (CMIM). The CMIM adaptively recalibrated the temporal- and axis-wise features in each modality by leveraging multi-modal information to achieve deep intermodality interaction. A class-balanced (CB) focal loss was adopted to supervise the training of CMI-Net to alleviate the class imbalance problem. Motion data was acquired from six neck-attached inertial measurement units from six horses. The CMI-Net was trained and verified with leave-one-out cross-validation. The results demonstrated that our CMI-Net outperformed the existing algorithms with high precision (79.74%), recall (79.57%), F1-score (79.02%), and accuracy (93.37%). The adoption of CB focal loss improved the performance of CMI-Net, with increases of 2.76%, 4.16%, and 3.92% in precision, recall, and F1-score, respectively. In conclusion, CMI-Net and CB focal loss effectively enhanced the equine activity classification performance using imbalanced multi-modal sensor data.

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Equine Welfare Assessment: Horse Motion Evaluation and Comparison to Manual Pain Measurements

Cited by 3 publications

References 13 publications

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Going Deeper than Tracking: A Survey of Computer-Vision Based Recognition of Animal Pain and Emotions

Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data

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