Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation

Rashid, Md. Harun-Or; Broomé, Sofia; Ask, Katrina; Hernlund, Elin; Andersen, Pia Haubro; Lee, Yong Jae

doi:10.1109/wacv51458.2022.00023

Cited by 12 publications

(21 citation statements)

References 51 publications

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“…In a more recent work of ours [49], we perform equine pain recognition on 3D pose representations extracted from multi-view surveillance data, on the same low grade orthopedic pain trial as in this paper. Although the horses and pain trial are the same as in the current work, the crucial difference is that the data used in [49] is different (surveillance data in the box, whereas here, we use videos recorded with a tripod outside the box, where the facial expression is visible), and that only the pose representation is used for classification. This is advantageous to reduce the amount of extraneous information.…”

Section: Automatic Pain Recognition In Animalsmentioning

confidence: 99%

“…However, the potential disadvantage is that any facial expressions are not possible to take into account. As a result, it is perhaps the adjustment of pose as a result of previous pain that is recognized in [49], rather than whether a pain experience is ongoing. Similarly to the present work, it is found that low grade orthopedic pain is difficult to detect, compared to the less noisy pain trial used in [22].…”

Section: Automatic Pain Recognition In Animalsmentioning

confidence: 99%

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Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses

et al. 2022

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Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.

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Section: Automatic Pain Recognition In Animalsmentioning

confidence: 99%

Section: Automatic Pain Recognition In Animalsmentioning

confidence: 99%

Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses

et al. 2022

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“…In this section, the meta-analysis of the works in Table 1 is organized according to the different stages of a typical workflow in studies within this domain: data collection and annotation, followed by data analysis (typically, model training and inference) and last, performance evaluation. For each of these stages, we classify the methods and techniques applied in these [76] unknown or naturally occurring face + Lencioni et al [77] Horses pain surgical castration face + Hummel et al [38] unknown or induced pain face + Broomé et al [78] induced pain body and face + Broomé et al [79] induced pain body and face + Rashid et al [80] induced pain body + Reulke et al [81] vet. procedure body -Corujo et al [82] emotion unknown body and face + Li et al [83] --face ---Feightelstein et al [84] Cats pain vet.…”

Section: Meta-analysis Of Computer Vision-based Approaches For Classi...mentioning

confidence: 99%

“…Infrared cameras placed on top of the face are also used, but these observe movement patterns, and not facial expressions. Equines are recorded in a box [80,94] from multi-view surveillance cameras, or in open areas, but with static cameras placed at a distance to capture the animal from the side [38,78,79,95], or frontally, when the animal is next to a feeder [77]. The side view makes observing the bodily behavior easier, but only one side of the face is visible.…”

Section: Data Collectionmentioning

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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“…The availability of such visual-temp data enables addressing it in two manners: (i) single frames and (ii) sequences of frames. The first implies more information loss, but is simpler and more controllable; while the latter includes temporal dimension, which has been shown to have importance for such tasks, e.g., in the context of detection of pain in horses [9,40]. The prevalent approach in the context of automated recognition of effective states and pain in animals, is, however, the single frame basis (e.g., [2,29,33,36]).…”

Section: Datasetmentioning

confidence: 99%

Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions

Boneh-Shitrit¹,

Amir²,

Bremhorst³

et al. 2022

Preprint

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Similarly to humans, facial expressions in animals are closely linked with emotional states. However, in contrast to the human domain, automated recognition of emotional states from facial expressions in animals is underexplored, mainly due to difficulties in data collection and establishment of ground truth concerning emotional states of nonverbal users. We apply recent deep learning techniques to classify (positive) anticipation and (negative) frustration of dogs on a dataset collected in a controlled experimental setting. We explore the suitability of different backbones (e.g. ResNet, ViT) under different supervisions to this task, and find that features of a self-supervised pretrained ViT (DINO-ViT) are superior to the other alternatives. To the best of our knowledge, this work is the first to address the task of automatic classification of canine emotions on data acquired in a controlled experiment.

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Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation

Cited by 12 publications

References 51 publications

Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses

Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses

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

Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions

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