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

Broomé, Sofia; Feighelstein, Marcelo; Zamansky, Anna; Lencioni, Gabriel Carreira; Andersen, Pia Haubro; Pessanha, Francisca; Mahmoud, Marwa; Salah, Albert Ali

doi:10.1007/s11263-022-01716-3

Cited by 23 publications

(10 citation statements)

References 132 publications

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“…As highlighted in ( 52 ), a major focus in computer vision is on the use of features that can be extracted from images. Features can be hand-crafted, or manually designed, or learnt, as is the case in deep learning paradigms.…”

Section: A Mapping Of Automated Approaches In Dog Behavioral Data Ana...mentioning

confidence: 99%

Automation in canine science: enhancing human capabilities and overcoming adoption barriers

Farhat,

van der Linden,

Zamansky

et al. 2024

Front. Vet. Sci.

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The emerging field of canine science has been slow in adopting automated approaches for data analysis. However, with the dramatic increase in the volume and complexity of the collected behavioral data, this is now beginning to change. This paper aims to systematize the field of automation in canine science. We provide an examination of current automation processes and pipelines by providing a literature review of state-of-the-art studies applying automation in this field. In addition, via an empirical study with researchers in animal behavior, we explore their perceptions and attitudes toward automated approaches for better understanding barriers for a wider adoption of automation. The insights derived from this research could facilitate more effective and widespread utilization of automation within canine science, addressing current challenges and enhancing the analysis of increasingly complex and voluminous behavioral data. This could potentially revolutionize the field, allowing for more objective and quantifiable assessments of dog behavior, which would ultimately contribute to our understanding of dog-human interactions and canine welfare.

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Section: A Mapping Of Automated Approaches In Dog Behavioral Data Ana...mentioning

confidence: 99%

Automation in canine science: enhancing human capabilities and overcoming adoption barriers

Farhat,

van der Linden,

Zamansky

et al. 2024

Front. Vet. Sci.

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“…Animal behavior is the body language by which an animal expresses its psychological and physiological state and its overall function. Typical model animals, such as mice, rabbits, and goats, are widely used to analyze different behaviors in the open field to measure the effectiveness of experiments in biology, toxicology, neuroscience, pharmacology, animal husbandry, and genetics [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

Chen,

Jiao,

Song

et al. 2024

Applied Sciences

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In recent years, with the rapid development of medicine, pathology, toxicology, and neuroscience technology, animal behavior research has become essential in modern life science research. However, the current mainstream commercial animal behavior recognition tools only provide a single behavior recognition method, limiting the expansion of algorithms and how researchers interact with experimental data. To address this issue, we propose an AI-enabled, highly usable platform for analyzing experimental animal behavior, which aims to provide better flexibility, scalability, and interactivity to make the platform more usable. Researchers can flexibly select or extend different behavior recognition algorithms for automated recognition of animal behaviors or experience more convenient human-computer interaction through natural language descriptions only. A case study at a medical laboratory where the platform was used to evaluate behavioral differences between sick and healthy animals demonstrated the high usability of the platform.

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“…Animal behavior encompasses a wealth of health-related information, capable of directly or indirectly reflecting an animal's physiological, pathological, nutritional, and other intrinsic states [2] . In recent years, the rapid advancements in computer vision and deep learning technologies have propelled non-contact and automated methods for animal behavior recognition based on visual features into the mainstream [3]. These methods can be categorized into two groups: those relying on overall image features and those based on skeleton features [4].…”

Section: Introduction and Related Workmentioning

confidence: 99%

A method for estimating pig pose in a breeding scenario

Qin,

Mao,

Wang

et al. 2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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Aiming at the problem of low automation in pig farms, this paper proposes a new pig posture estimation method based on breeding scenarios for intelligent monitoring of pig farms.Firstly, the video image data of indoor and outdoor scenes in pig breeding scenarios were collected and labeled, and a self-constructed pig posture estimation dataset was built; Secondly,Resnet50, VGG16 and MobileNetV2 were used as the back-bone network, and the three methods based on coordinate regression, heat map and simple coordinate classi-fication were analyzed experimentally, and the Simple Coordinate Classification (SimCC) algorithm with the optimal effect was selected as the extraction method of key points of pigs;Finally, we integrated High Resolution Network(HRNet)and HRFormer, which incorporates Transformer modules, as backbone net-works. They were combined with the SimCC to formulate an effective pig pose estimation framework. The experimental results show that the mAP of HRFormer-SimCC reaches 83.2%, which is an average im-provement of 7.2% over the use of traditional CNN model and 0.4% over the HRNet-SimCC, and the float-ing-point computation and parameter counts of HRFormer-SimCC are only 45.05% and 36.48% of it. This is more suitable to be deployed in breeding environments and provides a theoretical basis for intelligent moni-toring of pig farms.

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

Cited by 23 publications

References 132 publications

Automation in canine science: enhancing human capabilities and overcoming adoption barriers

Automation in canine science: enhancing human capabilities and overcoming adoption barriers

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

A method for estimating pig pose in a breeding scenario

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