Movement based social interactions with label uncertainty
Abstract
Drone-based imagery provides high resolution data for tracking wildlife populations. However, monitoring marine mammals like cetaceans is challenging because individuals frequently submerge into water or move out of the drone's field of view, resulting in disconnected trajectories due to uncertain identities. Existing social movement models assume individual identities are known, which is often only true when data come from physical tracking devices like GPS collars. To address this limitation, we propose a Bayesian hierarchical state-space model that treats the identities associated with observed locations as unknown random variables. This allows us to jointly estimate individual movement parameters while propagating the uncertainty about identity. We apply our framework to drone-measured movement data from Risso's dolphins (Grampus griseus) to estimate the effects of sonar exposure on the dolphins' movement behavior. Our proposed method offers a solution for analyzing collective animal behavior when continuous tracking of every individual is challenging.
Related
- Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentation
- A Label Noise Robust Algorithm for Graph Data using Neighborhood Label Distribution
- Optimal Multiclass Online Regret with Arbitrary Label Spaces
- Metabolic signatures accompany exploratory movement in climbing plants
- Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification
BibTeX
@misc{mathias2026movement,
title = {{Movement based social interactions with label uncertainty}},
author = {Anisha J. Mathias and John W. Durban and Holly Fearnbach and Samantha G. M. Leander and Henry R. Scharf},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00565},
eprint = {2611.00565},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00565},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Movement based social interactions with label uncertainty
AU - Anisha J. Mathias
AU - John W. Durban
AU - Holly Fearnbach
AU - Samantha G. M. Leander
AU - Henry R. Scharf
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00565
UR - https://arxiv.org/abs/2611.00565
AB - Drone-based imagery provides high resolution data for tracking wildlife populations. However, monitoring marine mammals like cetaceans is challenging because individuals frequently submerge into water or move out of the drone's field of view, resulting in disconnected trajectories due to uncertain identities. Existing social movement models assume individual identities are known, which is often only true when data come from physical tracking devices like GPS collars. To address this limitation, we propose a Bayesian hierarchical state-space model that treats the identities associated with observed locations as unknown random variables. This allows us to jointly estimate individual movement parameters while propagating the uncertainty about identity. We apply our framework to drone-measured movement data from Risso's dolphins (Grampus griseus) to estimate the effects of sonar exposure on the dolphins' movement behavior. Our proposed method offers a solution for analyzing collective animal behavior when continuous tracking of every individual is challenging.
ER -