A Review on Coarse to Fine-Grained Animal Action Recognition

• URL: http://arxiv.org/abs/2506.01214v1
• Date: Sun, 01 Jun 2025 23:31:25 GMT
• Title: A Review on Coarse to Fine-Grained Animal Action Recognition
• Authors: Ali Zia, Renuka Sharma, Abdelwahed Khamis, Xuesong Li, Muhammad Husnain, Numan Shafi, Saeed Anwar, Sabine Schmoelzl, Eric Stone, Lars Petersson, Vivien Rolland,
• Abstract summary: Review explores the field of animal action recognition, focusing on coarse-grained (FGCG) and fine-grained (FGG) techniques.<n>Examines the current state of research in animal behaviour recognition and to elucidate the unique challenges associated with recognising subtle animal actions in outdoor environments.<n>Review outlines future directions for advancing fine-grained action recognition, aiming to improve accuracy and generalisability in behaviour analysis across species.
• Score: 23.001797172183345
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This review provides an in-depth exploration of the field of animal action recognition, focusing on coarse-grained (CG) and fine-grained (FG) techniques. The primary aim is to examine the current state of research in animal behaviour recognition and to elucidate the unique challenges associated with recognising subtle animal actions in outdoor environments. These challenges differ significantly from those encountered in human action recognition due to factors such as non-rigid body structures, frequent occlusions, and the lack of large-scale, annotated datasets. The review begins by discussing the evolution of human action recognition, a more established field, highlighting how it progressed from broad, coarse actions in controlled settings to the demand for fine-grained recognition in dynamic environments. This shift is particularly relevant for animal action recognition, where behavioural variability and environmental complexity present unique challenges that human-centric models cannot fully address. The review then underscores the critical differences between human and animal action recognition, with an emphasis on high intra-species variability, unstructured datasets, and the natural complexity of animal habitats. Techniques like spatio-temporal deep learning frameworks (e.g., SlowFast) are evaluated for their effectiveness in animal behaviour analysis, along with the limitations of existing datasets. By assessing the strengths and weaknesses of current methodologies and introducing a recently-published dataset, the review outlines future directions for advancing fine-grained action recognition, aiming to improve accuracy and generalisability in behaviour analysis across species.

Related papers

• A Dataset for Semantic Segmentation in the Presence of Unknowns [49.795683850385956]
  Existing datasets allow evaluation of only knowns or unknowns - but not both.<n>We propose a novel anomaly segmentation dataset, ISSU, that features a diverse set of anomaly inputs from cluttered real-world environments.<n>The dataset is twice larger than existing anomaly segmentation datasets.
  arXiv  Detail & Related papers  (2025-03-28T10:31:01Z)
• Categorical Keypoint Positional Embedding for Robust Animal Re-Identification [22.979350771097966]
  Animal re-identification (ReID) has become an indispensable tool in ecological research.<n>Unlike human ReID, animal ReID faces significant challenges due to the high variability in animal poses, diverse environmental conditions, and the inability to directly apply pre-trained models to animal data.<n>This work introduces an innovative keypoint propagation mechanism, which utilizes a single annotated pre-trained diffusion model.
  arXiv  Detail & Related papers  (2024-12-01T14:09:00Z)
• Animal Behavior Analysis Methods Using Deep Learning: A Survey [3.9086052572489653]
  State-of-the-art deep learning models have demonstrated remarkable accuracy in classifying various forms of animal data. 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.
  arXiv  Detail & Related papers  (2024-05-22T21:17:54Z)
• Computer Vision for Primate Behavior Analysis in the Wild [61.08941894580172]
  Video-based behavioral monitoring has great potential for transforming how we study animal cognition and behavior. There is still a fairly large gap between the exciting prospects and what can actually be achieved in practice today.
  arXiv  Detail & Related papers  (2024-01-29T18:59:56Z)
• Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances [76.34037366117234]
  We introduce a new dataset called Robot Control Gestures (RoCoG-v2) The dataset is composed of both real and synthetic videos from seven gesture classes. We present results using state-of-the-art action recognition and domain adaptation algorithms.
  arXiv  Detail & Related papers  (2023-03-17T23:23:55Z)
• Dataset Bias in Human Activity Recognition [57.91018542715725]
  This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance. We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
  arXiv  Detail & Related papers  (2023-01-19T12:33:50Z)
• CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey [0.0]
  Action recognition, classifying activities performed by one or more subjects in a trimmed video, forms the basis of many techniques. Deep learning models for human action recognition have progressed over the last decade. Recent interest in research that incorporates deep learning-based action recognition for classification has increased.
  arXiv  Detail & Related papers  (2023-01-15T20:54:44Z)
• Review on Social Behavior Analysis of Laboratory Animals: From Methodologies to Applications [0.0]
  We explore a variety of behaviour detection algorithms, covering traditional vision methods, statistical methods and deep learning methods. The objective of this work is to provide a thorough investigation of related work, furnishing biologists with a scratch of efficient animal behaviour detection methods.
  arXiv  Detail & Related papers  (2022-06-25T13:40:35Z)
• Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States [1.993938356023085]
  An increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain. This paper provides a comprehensive survey of computer vision-based research on recognition of affective states and pain in animals.
  arXiv  Detail & Related papers  (2022-06-16T18:50:02Z)
• Persistent Animal Identification Leveraging Non-Visual Markers [71.14999745312626]
  We aim to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
  arXiv  Detail & Related papers  (2021-12-13T17:11:32Z)
• Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework [83.21732533130846]
  The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2. It presents the design of two classes of deep neural networks trained with these databases. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
  arXiv  Detail & Related papers  (2021-03-29T17:36:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.