Identity-stable multi-animal tracking using bidirectional segmentation with object-level memory.

ABSTRACT: Tracking animal behavior in naturalistic settings is essential for understanding social dynamics and their neural underpinnings. Pose estimation methods can produce accurate keypoints using framewise inference. However, post hoc tracking steps often struggle to maintain consistent identity over time, particularly during close and rapid social interactions between visually similar animals. We present a pipeline for bidirectional video object segmentation (VOS) to correct identity swaps with much less manual annotation effort, addressing the prohibitive cost of identity correction of pose estimation data in long recordings and large cohorts. Our approach makes use of a state-of-the-art VOS algorithm, Cutie, which leverages both pixel- and object-level representations across multiple memory t

SUBMITTER: Wang S 

PROVIDER: S-EPMC12485842 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature