ReviewNature methods2025
Computer vision for primate behavior analysis in the wild.
Review in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- From pixels to conservation: Deep learning for automated monitoring of rare and endangered wildlife.iScience · 2026Article
- A Smart-Alarm Detection and Deterrent System for Long-Tailed Macaque Intrusion in Human Residential Zones.Journal of medical primatology · 2026Article
- Realistic monkey body animation reveals an uncanny valley in macaque body perception.PLoS biology · 2026Article
- Designing research interfaces for non-human minds: a biocentric design approach for animal cognition research.Animal cognition · 2026Review
- Unsupervised transfer learning enables multi-animal tracking without training annotation.Nature methods · 2026Article
- Article
- Assessing attentiveness and cognitive engagement across tasks using video-based action understanding in non-human primates.Journal of neuroscience methods · 2025Article
- The Search for Love in Human Evolution: Primate Social Bonds and a New Science of Emotion.American journal of biological anthropology · 2025Review
- The impact of machine learning on ethological neuroscience.Frontiers in behavioral neuroscience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Advances in computer vision and increasingly widespread video-based behavioral monitoring are currently transforming how we study animal behavior. However, there is still a gap between the prospects and practical application, especially in videos from the wild. In this Perspective, we aim to present the capabilities of current methods for behavioral analysis, while at the same time highlighting unsolved computer vision problems that are relevant to the study of animal behavior. We survey state-of-the-art methods for computer vision problems relevant to the video-based study of individualized animal behavior, including object detection, multi-animal tracking, individual identification and (inter)action understanding. We then review methods for effort-efficient learning, one of the challenges from a practical perspective. In our outlook on the emerging field of computer vision for animal behavior, we argue that the field should develop approaches to unify detection, tracking, identification and (inter)action understanding in a single, video-based framework.
Indexed as
Identifiers
40211003What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.