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Animal Behavior Analysis Methods Using Deep Learning: A Survey (2405.14002v1)

Published 22 May 2024 in cs.LG

Abstract: Animal behavior serves as a reliable indicator of the adaptation of organisms to their environment and their overall well-being. Through rigorous observation of animal actions and interactions, researchers and observers can glean valuable insights into diverse facets of their lives, encompassing health, social dynamics, ecological relationships, and neuroethological dimensions. Although state-of-the-art deep learning models have demonstrated remarkable accuracy in classifying various forms of animal data, their adoption in animal behavior studies remains limited. This survey article endeavors to comprehensively explore deep learning architectures and strategies applied to the identification of animal behavior, spanning auditory, visual, and audiovisual methodologies. Furthermore, the manuscript scrutinizes extant animal behavior datasets, offering a detailed examination of the principal challenges confronting this research domain. The article culminates in a comprehensive discussion of key research directions within deep learning that hold potential for advancing the field of animal behavior studies.

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References (210)
  1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information fusion 76 (2021), 243–297.
  2. Automated bird counting with deep learning for regional bird distribution mapping. Animals 10, 7 (2020), 1207.
  3. Drones for sheep livestock monitoring. In 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON). IEEE, 672–676.
  4. Mohammed Alagele and Remzi Yildirim. 2022. ANIMAL GAIT IDENTIFICATION USING A DEEP LEARNING METHOD. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 540–542.
  5. Automated detection and quantification of contact behaviour in pigs using deep learning. biosystems engineering 224 (2022), 118–130.
  6. Automatic recognition of feeding and foraging behaviour in pigs using deep learning. Biosystems engineering 197 (2020), 91–104.
  7. Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis. PLoS One 15, 4 (2020), e0228059.
  8. Animal behavior classification via deep learning on embedded systems. Computers and Electronics in Agriculture 207 (2023), 107707.
  9. Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data. Smart Agricultural Technology 4 (2023), 100163.
  10. Inferring learning rules from animal decision-making. Advances in Neural Information Processing Systems 33 (2020), 3442–3453.
  11. Dairy cow rumination detection: A deep learning approach. In Distributed Computing for Emerging Smart Networks: Second International Workshop, DiCES-N 2020, Bizerte, Tunisia, December 18, 2020, Proceedings 2. Springer, 123–139.
  12. Deep-worm-tracker: Deep learning methods for accurate detection and tracking for behavioral studies in C. elegans. Applied Animal Behaviour Science 266 (2023), 106024.
  13. Jun Bao and Qiuju Xie. 2022. Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production 331 (2022), 129956.
  14. Quick, accurate, smart: 3D computer vision technology helps assessing confined animals’ behaviour. PloS one 11, 7 (2016), e0158748.
  15. Richard Bellman. 1957. A Markovian Decision Process. Journal of Mathematics and Mechanics 6, 5 (1957), 679–684. http://www.jstor.org/stable/24900506
  16. Improved cattle behaviour monitoring by combining Ultra-Wideband location and accelerometer data. animal 17, 4 (2023), 100730.
  17. Classifying behavior from short-interval biologging data: An example with GPS tracking of birds. Ecology and Evolution 12, 2 (2022), e08395.
  18. Mapping the stereotyped behaviour of freely moving fruit flies. Journal of The Royal Society Interface 11, 99 (2014), 20140672.
  19. Ethoflow: computer vision and artificial intelligence-based software for automatic behavior analysis. Sensors 21, 9 (2021), 3237.
  20. Samayan Bhattacharya and Sk Shahnawaz. 2021. Pose recognition in the wild: Animal pose estimation using agglomerative clustering and contrastive learning. arXiv preprint arXiv:2111.08259 (2021).
  21. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools. bioRxiv (2023).
  22. Accurate detection and identification of insects from camera trap images with deep learning. PLOS Sustainability and Transformation 2, 3 (2023), e0000051.
  23. SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework. arXiv preprint arXiv:2204.07072 (2022).
  24. On the benefits of deep convolutional neural networks on animal activity recognition. In 2020 International Conference on Smart Systems and Technologies (SST). IEEE, 83–88.
  25. DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels. Elife 10 (2021), e63377.
  26. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory. 144–152.
  27. Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture. Scientific Reports 11, 1 (2021), 15733.
  28. Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5–32.
  29. André EX Brown and Benjamin de Bivort. 2017. The study of animal behaviour as a physical science. bioRxiv (2017). https://doi.org/10.1101/220855 arXiv:https://www.biorxiv.org/content/early/2017/11/17/220855.full.pdf
  30. Elizabeth C Bryda. 2013. The Mighty Mouse: the impact of rodents on advances in biomedical research. Missouri medicine 110, 3 (2013), 207.
  31. Social behavior recognition in continuous video. In 2012 IEEE conference on computer vision and pattern recognition. IEEE, 1322–1329.
  32. Cross-domain adaptation for animal pose estimation. In Proceedings of the IEEE/CVF international conference on computer vision. 9498–9507.
  33. Real-time robust detector for underwater live crabs based on deep learning. Computers and Electronics in Agriculture 172 (2020), 105339.
  34. Realtime multi-person 2d pose estimation using part affinity fields. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7291–7299.
  35. Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–7.
  36. Deep learning classification of canine behavior using a single collar-mounted accelerometer: Real-world validation. Animals 11, 6 (2021), 1549.
  37. Behaviour recognition of pigs and cattle: Journey from computer vision to deep learning. Computers and Electronics in Agriculture 187 (2021), 106255.
  38. Recognition of feeding behaviour of pigs and determination of feeding time of each pig by a video-based deep learning method. Computers and Electronics in Agriculture 176 (2020), 105642.
  39. Adversarial posenet: A structure-aware convolutional network for human pose estimation. In Proceedings of the IEEE international conference on computer vision. 1212–1221.
  40. Beyond tracking: using deep learning to discover novel interactions in biological swarms. Artificial Life and Robotics 27, 2 (2022), 393–400.
  41. Cat Monitoring and Disease Diagnosis System based on Deep Learning. Journal of Korea Multimedia Society 24, 2 (2021), 233–244.
  42. François Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1251–1258.
  43. Self adversarial training for human pose estimation. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 17–30.
  44. The neurobiology of behavior and its applicability for animal welfare: A review. Animals 12, 7 (2022), 928.
  45. Explainable deep convolutional neural networks for insect pest recognition. Journal of Cleaner Production 371 (2022), 133638.
  46. A LoRaWAN-Based Smart Sensor Tag for Cow Behavior Monitoring. In 2022 IEEE Sensors. IEEE, 1–4.
  47. Automated image-based tracking and its application in ecology. Trends in ecology & evolution 29, 7 (2014), 417–428.
  48. Automating the analysis of fish abundance using object detection: optimizing animal ecology with deep learning. Frontiers in Marine Science (2020), 429.
  49. Resampling and data augmentation for equines’ behaviour classification based on wearable sensor accelerometer data using a convolutional neural network. In 2020 International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 1–6.
  50. The Use of Pose Estimation for Abnormal Behavior Analysis in Poultry Farms. In 2023 5th Novel Intelligent and Leading Emerging Sciences Conference (NILES). IEEE, 33–36.
  51. YOLOv7 and DeepSORT for Intelligent Quail Behavioral Activities Monitoring. In 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 1–5.
  52. Combining local appearance and holistic view: Dual-source deep neural networks for human pose estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1347–1355.
  53. Pose estimation and behavior classification of broiler chickens based on deep neural networks. Computers and Electronics in Agriculture 180 (2021), 105863.
  54. Using AI to decode the behavioral responses of an insect to chemical stimuli: towards machine-animal computational technologies. International Journal of Machine Learning and Cybernetics (2023). https://doi.org/10.1007/s13042-023-02009-y
  55. A Workflow for Developing Biohybrid Intelligent Sensing Systems. (2022).
  56. Slowfast networks for video recognition. In Proceedings of the IEEE/CVF international conference on computer vision. 6202–6211.
  57. Deep learning for video-based automated pain recognition in rabbits. Scientific Reports 13, 1 (2023), 14679.
  58. Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information. Computers and Electronics in Agriculture 177 (2020), 105627.
  59. Animal behavior classification using DeepLabCut. In 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE). IEEE, 254–257.
  60. Rodent Tracking and Abnormal Behavior Classification in Live Video using Deep Neural Networks. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 830–837.
  61. Generative adversarial nets. Advances in neural information processing systems 27 (2014).
  62. Zebrafish Larvae Position Tracker (Z-LaP Tracker): a high-throughput deep-learning behavioral approach for the identification of calcineurin pathway-modulating drugs using zebrafish larvae. Scientific Reports 13, 1 (2023), 3174.
  63. Animal behavior facilitates eco-evolutionary dynamics. arXiv preprint arXiv:1912.09505 (2019).
  64. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. Elife 8 (2019), e47994.
  65. Towards image-based animal tracking in natural environments using a freely moving camera. Journal of neuroscience methods 330 (2020), 108455.
  66. Fish shoals behavior detection based on convolutional neural network and spatiotemporal information. IEEE Access 8 (2020), 126907–126926.
  67. Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 6546–6555.
  68. Benjamin L Hart. 2011. Behavioural defences in animals against pathogens and parasites: parallels with the pillars of medicine in humans. Philosophical Transactions of the Royal Society B: Biological Sciences 366, 1583 (2011), 3406–3417.
  69. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision. 2961–2969.
  70. Identity mappings in deep residual networks. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. Springer, 630–645.
  71. Human activity recognition using marine predators algorithm with deep learning. Future Generation Computer Systems 142 (2023), 340–350.
  72. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
  73. A Bayesian approach for quantifying data scarcity when modeling human behavior via inverse reinforcement learning. ACM Transactions on Computer-Human Interaction 30, 1 (2023), 1–27.
  74. Identification of animal individuals using deep learning: A case study of giant panda. Biological Conservation 242 (2020), 108414.
  75. Multi-level feature fusion for multimodal human activity recognition in Internet of Healthcare Things. Information Fusion 94 (2023), 17–31.
  76. Inceptiontime: Finding alexnet for time series classification. Data Mining and Knowledge Discovery 34, 6 (2020), 1936–1962.
  77. Selfee, self-supervised features extraction of animal behaviors. Elife 11 (2022), e76218.
  78. Animal pose estimation: A closer look at the state-of-the-art, existing gaps and opportunities. Computer Vision and Image Understanding (2022), 103483.
  79. Automatic behavior recognition of group-housed goats using deep learning. Computers and Electronics in Agriculture 177 (2020), 105706.
  80. Detecting and tracking of multiple mice using part proposal networks. IEEE Transactions on Neural Networks and Learning Systems (2022).
  81. Multi-view mouse social behaviour recognition with deep graphic model. IEEE Transactions on Image Processing 30 (2021), 5490–5504.
  82. Birds’ Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being. arXiv preprint arXiv:2205.00069 (2022).
  83. Telmo De Cesaro Júnior and Rafael Rieder. 2020. Automatic identification of insects from digital images: A survey. Computers and Electronics in Agriculture 178 (2020), 105784.
  84. Robust sensor-orientation-independent feature selection for animal activity recognition on collar tags. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 1–27.
  85. Dataset: Horse movement data and analysis of its potential for activity recognition. In Proceedings of the 2nd Workshop on Data Acquisition To Analysis. 22–25.
  86. Deep learning empowered wearable-based behavior recognition for search and rescue dogs. Sensors 22, 3 (2022), 993.
  87. Jörg Kaufmann and AG Schering. 2014. Analysis of Variance ANOVA. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118445112.stat06938 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118445112.stat06938
  88. Disease detection in pigs based on feeding behaviour traits using machine learning. biosystems engineering 226 (2023), 132–143.
  89. Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).
  90. Deep transfer learning in sheep activity recognition using accelerometer data. Expert Systems with Applications 207 (2022), 117925.
  91. A survey of machine learning approaches in animal behaviour. Neurocomputing 491 (2022), 442–463.
  92. Elly C Knight and Erin M Bayne. 2019. Classification threshold and training data affect the quality and utility of focal species data processed with automated audio-recognition software. Bioacoustics 28, 6 (2019), 539–554.
  93. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop, Vol. 2. Lille.
  94. Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision. Journal of Animal Ecology (2023).
  95. Standardized and reproducible measurement of decision-making in mice. Elife 10 (2021), e63711.
  96. MacaquePose: a novel “in the wild” macaque monkey pose dataset for markerless motion capture. Frontiers in behavioral neuroscience 14 (2021), 581154.
  97. Animal-in-the-loop: using interactive robotic conspecifics to study social behavior in animal groups. Annual Review of Control, Robotics, and Autonomous Systems 4 (2021), 487–507.
  98. Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods 19, 4 (2022), 496–504.
  99. Temporal convolutional networks: A unified approach to action segmentation. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14. Springer, 47–54.
  100. A validation of supervised deep learning for gait analysis in the cat. Frontiers in Neuroinformatics 15 (2021), 712623.
  101. Predicting Fruit Fly Behaviour using TOLC device and DeepLabCut. In 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 1–6.
  102. Postural behavior recognition of captive nocturnal animals based on deep learning: a case study of Bengal slow loris. Scientific Reports 12, 1 (2022), 7738.
  103. Chen Li and Gim Hee Lee. 2023. ScarceNet: Animal Pose Estimation with Scarce Annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 17174–17183.
  104. A spatiotemporal convolutional network for multi-behavior recognition of pigs. Sensors 20, 8 (2020), 2381.
  105. Detection and analysis of behavior trajectory for sea cucumbers based on deep learning. Ieee Access 8 (2019), 18832–18840.
  106. ATRW: a benchmark for Amur tiger re-identification in the wild. arXiv preprint arXiv:1906.05586 (2019).
  107. Human activity recognition based on multienvironment sensor data. Information Fusion 91 (2023), 47–63.
  108. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2117–2125.
  109. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, 413–422.
  110. OptiFlex: video-based animal pose estimation using deep learning enhanced by optical flow. BioRxiv (2020), 2020–04.
  111. A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping. Robotics and Computer-Integrated Manufacturing 78 (2022), 102365.
  112. Long-distance detection of bioacoustic events with per-channel energy normalization. arXiv preprint arXiv:1911.00417 (2019).
  113. Identifying behavioral structure from deep variational embeddings of animal motion. Communications Biology 5, 1 (2022), 1267.
  114. A systematic literature review on deep learning applications for precision cattle farming. Computers and Electronics in Agriculture 187 (2021), 106313.
  115. Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant. Iscience 26, 12 (2023).
  116. Automated applications of acoustics for stored product insect detection, monitoring, and management. Insects 12, 3 (2021), 259.
  117. Dr Samuel Manoharan. 2020. Embedded imaging system based behavior analysis of dairy cow. Journal of Electronics and Informatics 2, 2 (2020), 148–154.
  118. Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments. Nature machine intelligence 4, 4 (2022), 331–340.
  119. Leaving flatland: Advances in 3D behavioral measurement. Current Opinion in Neurobiology 73 (2022), 102522.
  120. Jellytoring: real-time jellyfish monitoring based on deep learning object detection. Sensors 20, 6 (2020), 1708.
  121. Pretraining boosts out-of-domain robustness for pose estimation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 1859–1868.
  122. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature neuroscience 21, 9 (2018), 1281–1289.
  123. Mackenzie Weygandt Mathis and Alexander Mathis. 2020. Deep learning tools for the measurement of animal behavior in neuroscience. Current opinion in neurobiology 60 (2020), 1–11.
  124. Movement tracks for the automatic detection of fish behavior in videos. arXiv preprint arXiv:2011.14070 (2020).
  125. Capturing continuous, long timescale behavioral changes in Drosophila melanogaster postural data. arXiv preprint arXiv:2309.04044 (2023).
  126. ResNet-based Deep Neural Network using Transfer Learning for Animal Activity Recognition. In 2022 6th International Conference on Information Technology (InCIT). IEEE, 445–449.
  127. Application of Machine Learning for Insect Monitoring in Grain Facilities. AI 4, 1 (2023), 348–360.
  128. Shailendra Mishra and Sunil Kumar Sharma. 2023. Advanced contribution of IoT in agricultural production for the development of smart livestock environments. Internet of Things 22 (2023), 100724.
  129. Ant colonies maintain social homeostasis in the face of decreased density. Elife 8 (2019), e38473.
  130. Chicken Behavior Analysis for Surveillance in Poultry Farms. (2023).
  131. NIPS4Bplus: a richly annotated birdsong audio dataset. PeerJ Computer Science 5 (2019), e223.
  132. Probabilistic generative modeling and reinforcement learning extract the intrinsic features of animal behavior. Neural Networks 145 (2022), 107–120.
  133. Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection. arXiv preprint arXiv:2309.08971 (2023).
  134. Estimating the Feeding Time of Individual Broilers via Convolutional Neural Network and Image Processing. Animals 13, 15 (2023), 2428.
  135. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature protocols 14, 7 (2019), 2152–2176.
  136. Suresh Neethirajan. 2020. The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research 29 (2020), 100367.
  137. Animal kingdom: A large and diverse dataset for animal behavior understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 19023–19034.
  138. Thong Duy Nguyen and Milan Kresovic. 2022. A survey of top-down approaches for human pose estimation. arXiv preprint arXiv:2202.02656 (2022).
  139. Simple Behavioral Analysis (SimBA)–an open source toolkit for computer classification of complex social behaviors in experimental animals. BioRxiv (2020), 2020–04.
  140. Learning to detect an animal sound from five examples. Ecological Informatics 77 (2023), 102258.
  141. Video Analysis using Deep Learning for Automatic Quantification of Ear Biting in Pigs. IEEE Access (2023).
  142. A review of deep learning algorithms for computer vision systems in livestock. Livestock Science 253 (2021), 104700.
  143. Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm. Diagnostics 13, 3 (2023), 433.
  144. A CNN-Based Animal Behavior Recognition Algorithm for Wearable Devices. IEEE Sensors Journal 23, 5 (2023), 5156–5164.
  145. PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model. In Proceedings of the European Conference on Computer Vision (ECCV).
  146. Predicting long-term collective animal behavior with deep learning. bioRxiv (2023), 2023–02.
  147. MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications. Future Generation Computer Systems 139 (2023), 196–209.
  148. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 1–22.
  149. Rosiglitazone attenuates learning and memory deficits in Tg2576 Alzheimer mice. Experimental neurology 199, 2 (2006), 265–273.
  150. Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2226–2234.
  151. Fast animal pose estimation using deep neural networks. Nature methods 16, 1 (2019), 117–125.
  152. SLEAP: A deep learning system for multi-animal pose tracking. Nature methods 19, 4 (2022), 486–495.
  153. Michael Perez and Corey Toler-Franklin. 2023. CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey. arXiv preprint arXiv:2301.06187 (2023).
  154. Tuan D Pham. 2022. Classification of Caenorhabditis Elegans Locomotion Behaviors With Eigenfeature-Enhanced Long Short-Term Memory Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 20, 1 (2022), 206–216.
  155. AJ Piergiovanni and Michael Ryoo. 2019. Temporal gaussian mixture layer for videos. In International Conference on Machine learning. PMLR, 5152–5161.
  156. A Deep Learning Approach for Detection and Classification of Ten Species of Monkeys. In 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 1–6.
  157. Lawrence Rabiner and Biinghwang Juang. 1986. An introduction to hidden Markov models. ieee assp magazine 3, 1 (1986), 4–16.
  158. Fast action recognition using negative space features. Expert Systems with Applications 41, 2 (2014), 574–587. https://doi.org/10.1016/j.eswa.2013.07.082
  159. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779–788.
  160. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28 (2015).
  161. Automatically detecting pig position and posture by 2D camera imaging and deep learning. Computers and Electronics in Agriculture 174 (2020), 105391. https://doi.org/10.1016/j.compag.2020.105391
  162. Mapping the global distribution of livestock. PloS one 9, 5 (2014), e96084.
  163. Donato Romano and Cesare Stefanini (Eds.). 2021. Biological Cybernetics. 115, issue 6 Animal-robot interaction and biohybrid organisms (2021).
  164. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234–241.
  165. Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017).
  166. T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information. Computers and Electronics in Agriculture 192 (2022), 106559.
  167. Adaptive uncertainty distribution in deep learning for unsupervised underwater image enhancement. arXiv preprint arXiv:2212.08983 (2022).
  168. A Pharmacology Toolkit for Animal Pose Estimation, Tracking and Analysis. In 2023 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 1–7.
  169. Zebrafish larvae locomotor activity detection using Convolutional Neural Network (CNN). (2022).
  170. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4510–4520.
  171. Learnable latent embeddings for joint behavioural and neural analysis. Nature (2023), 1–9.
  172. The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice. Elife 10 (2021), e63720.
  173. Grad-CAM: Why did you say that? arXiv preprint arXiv:1611.07450 (2016).
  174. Short distance migrants travel as far as long distance migrants in lesser black-backed gulls Larus fuscus. Journal of Avian Biology 48, 1 (2017), 49–57.
  175. Julie K Shaw and Sarah Lahrman. 2023. The human–animal bond–a brief look at its richness and complexities. Canine and Feline Behavior for Veterinary Technicians and Nurses (2023), 88–105.
  176. Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
  177. Dan Stowell. 2022. Computational bioacoustics with deep learning: a review and roadmap. PeerJ 10 (2022), e13152.
  178. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1–9.
  179. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818–2826.
  180. A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Computers and Electronics in Agriculture 182 (2021), 106030.
  181. A Systematic Review on Automatic Insect Detection Using Deep Learning. Agriculture 13, 3 (2023), 713.
  182. Vu Quang Thanh and Chayakorn Netramai. 2022. Deep learning-based monitoring system for distress on mice using behavior analysis. In 2022 International Electrical Engineering Congress (iEECON). IEEE, 1–4.
  183. Directpose: Direct end-to-end multi-person pose estimation. arXiv preprint arXiv:1911.07451 (2019).
  184. Novel use of FDA-approved drugs identified by cluster analysis of behavioral profiles. Scientific Reports 12, 1 (2022), 6120.
  185. Tom Uchino and Hayato Ohwada. 2021. Individual identification model and method for estimating social rank among herd of dairy cows using YOLOv5. In 2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC). IEEE, 235–241.
  186. An efficient approach for crops pests recognition and classification based on novel DeepPestNet deep learning model. IEEE Access 10 (2022), 73019–73032.
  187. Acoustic Classification of Insects using Signal Processing and Deep Learning Approaches. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 1048–1052.
  188. Real-time behavior detection and judgment of egg breeders based on YOLO v3. Neural Computing and Applications 32 (2020), 5471–5481.
  189. Identification and classification for sheep foraging behavior based on acoustic signal and deep learning. Computers and Electronics in Agriculture 187 (2021), 106275.
  190. Behavioral Recognition of Mice Based on a Deep Network. In 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 840–844.
  191. Deep learning-based behavioral profiling of rodent stroke recovery. BMC biology 20, 1 (2022), 1–19.
  192. Machine learning for modeling animal movement. Plos one 15, 7 (2020), e0235750.
  193. Supervised machine learning aided behavior classification in pigeons. Behavior Research Methods 55, 4 (2023), 1624–1640.
  194. Simple online and realtime tracking with a deep association metric. In 2017 IEEE international conference on image processing (ICIP). IEEE, 3645–3649.
  195. Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. Advances in Neural Information Processing Systems 33 (2020), 6040–6052.
  196. Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4820–4828.
  197. Multi-view Tracking, Re-ID, and Social Network Analysis of a Flock of Visually Similar Birds in an Outdoor Aviary. International Journal of Computer Vision 131, 6 (2023), 1532–1549.
  198. A multi-view CNN-based acoustic classification system for automatic animal species identification. Ad Hoc Networks 102 (2020), 102115.
  199. Vitpose: Simple vision transformer baselines for human pose estimation. Advances in Neural Information Processing Systems 35 (2022), 38571–38584.
  200. ViTPose++: Vision Transformer for Generic Body Pose Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023).
  201. Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector. Computers and Electronics in Agriculture 178 (2020), 105754.
  202. Evolution of a complex predator-prey ecosystem on large-scale multi-agent deep reinforcement learning. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8.
  203. Identification of animal behavioral strategies by inverse reinforcement learning. PLoS computational biology 14, 5 (2018), e1006122.
  204. Apt-36k: A large-scale benchmark for animal pose estimation and tracking. Advances in Neural Information Processing Systems 35 (2022), 17301–17313.
  205. SuperAnimal models pretrained for plug-and-play analysis of animal behavior. arXiv preprint arXiv:2203.07436 (2022).
  206. AP-10K: A Benchmark for Animal Pose Estimation in the Wild. arXiv:2108.12617 [cs.CV]
  207. Automated video behavior recognition of pigs using two-stream convolutional networks. Sensors 20, 4 (2020), 1085.
  208. Deep learning-based human pose estimation: A survey. Comput. Surveys 56, 1 (2023), 1–37.
  209. Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour. arXiv preprint arXiv:2208.03819 (2022).
  210. Hidden two-stream convolutional networks for action recognition. In Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14. Springer, 363–378.
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