PantherAI: An autonomous behavioural monitoring tool for assessing activity budget and space use in a zoo-housed tiger
Bibliographic record
Abstract
Machine learning (ML)-aided technologies can be applied to many of the existing wildlife science tools (e.g., camera traps) used to support conservation initiatives both in situ and ex situ. The automated nature of ML methods reduces manual labour, extends monitoring efforts past regular daylight/working hours, and improves the overall diagnostic capacity of tools routinely applied by wildlife biologists and animal care staff at zoological institutions. Though the conservation aims and expectations may differ among zoos and aquariums, simple monitoring tools that impose less demand on animal care staff should serve as an important aid for advancing management strategies for threatened species. We applied computer vision-based predictive models built on CCTV footage from a zoo-housed Panthera tigris individual to develop an automated behavioural monitoring tool (“PantherAI”) capable of rapidly assessing activity budget and space use across variable lighting and weather conditions. We applied YOLOv8 as the model backbone to detect and classify several tiger behaviours (e.g., stereotypical pacing, resting, enrichment interaction, feeding); the trained models were then applied with scripts to autonomously generate customized activity budgets and space use heatmaps from 24-h video samples. PantherAI yielded a mean average precision >75 % on test data, where it detected and classified tiger behaviours with varying levels of accuracy (stereotypical pacing: 92.2 %, resting: 72.2 %, locomotion: 65.4 %, feeding: 34.4 %, object manipulation: 43.8 %). Activity budgets varied ( p < 0.05) across habitats and by time of day for several behaviours. PantherAI provided reliable estimates of behaviour and space usage, two important ecological metrics commonly used to establish baseline activity budgets and assess indicators of animal welfare. Overall, ML-coupled technologies can facilitate daily data collection and monitoring procedures, both of which are integral for objectively measuring behavioural outcomes as newly implemented husbandry practices (e.g., alterations to diet, environment, social group, enrichment) are enacted in zoological and other ex situ conservation settings. • PantherAI enables rapid automated assessments of tiger behaviour from CCTV. • The monitoring system can be applied to recorded videos and live streams. • PantherAI offers a simple pipeline for assessing activity budget and space use. • Activity budget for many tiger behaviours differed by habitat area and time of day. • Detection accuracy exceeded 75 % for localizing and classifying tiger behaviours.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".