DIPLOMAT: multi-animal tracking with efficient manual editing
Bibliographic record
Abstract
Abstract Recent advances in computer vision have enabled the development of automated animal behavior observation tools. Several software packages currently exist for concurrently tracking pose in multiple animals; however, existing tools still face challenges in maintaining animal identities across frames and can demand extensive human oversight and editing. Here we report on DIPLOMAT, a D eep learning-based, I dentity- P reserving, L abeled- O bject M ulti- A nimal T racker, which implements automated algorithms improving tolerance to occlusion and continuity of animal identity over a video, further supplemented by an efficient human interface to help eliminate remaining errors. DIPLO-MAT is designed to perform multi-animal tracking by building on the per-frame pose prediction models of two state-of-the-art tools, DeepLabCut and SLEAP. Where other tools immediately take a maximum likelihood estimate from a given video frame, DIPLO-MAT splits the probability fields according to the number of tracked animals and then applies an inference method that takes into account across-frame movement and probabilistic distances between body parts. These independent trace probabilities are then preserved for human editing, enabling multiple body parts to be re-tracked across frames with minimal user action. Testing with a standardized and independently tracked dataset of 3-mouse videos shows DIPLOMAT’s automated components alone can reduce identity swaps by > 75%. DIPLOMAT code and documentation are available at https://diplomattrack.org/ . Significance Statement Tracking multiple animals during social behavior is essential for understanding neural mechanisms underlying social interaction, yet current tools frequently lose individual identities when animals approach/overlap. This identity confusion can require users to spend hours manually correcting tracking errors. DIPLOMAT addresses this bottleneck by integrating neural network and HMM approaches, combining per-frame body-part probabilities with probabilities related to inter-body-part distances and movement through space. We find this reduces identity swap errors without sacrificing other metrics. Moreover, DIPLOMAT stores probability data for use in a manual editing tool that allows for multi-body-part, manual re-tracking and error correction. DIPLOMAT therefore reduces user-time requirements for processing multi-animal recordings, which has the potential to accelerate research into the neural and genetic basis of social behavior.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".