[Submitted on 29 Sep 2026]
Title:TrackFish3D: Self-Supervised 3D Tracking of Schooling Fish from Multi-view Videos
View a PDF of the paper titled TrackFish3D: Self-Supervised 3D Tracking of Schooling Fish from Multi-view Videos, by Patt Phurtivilai and 7 other authors
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Abstract:Quantifying collective fish behavior requires accurate trajectories, yet multi-view 3D tracking remains challenging due to frequent occlusions, visually similar individuals, and the long-standing scarcity of identity annotations. We present TrackFish3D, a geometry-driven self-supervised framework for dense multi-camera 3D tracking of schooling fish. Instead of relying on appearance-based re-identification or manually annotated identities, TrackFish3D turns calibrated multi-view geometry into supervision: triangulation and reprojection consistency provide pseudo-associations, while a geometric encoder and global association transformer learn all-to-all cross-view correspondence within each frame. To make these associations identity-aware, TrackFish3D introduces a self-supervised contrastive objective that separates co-visible individuals in the embedding space, together with a temporal predictor that preserves identities and bridges short occlusions across frames. The resulting model is trained once on unlabeled footage and applied directly to unseen test videos, requiring no cross-view identity labels, temporal annotations, 3D ground truth, appearance features, or test-time optimization. On our benchmark, TrackFish3D improves 3D Multi-Object Tracking Accuracy from 87.7% for the strongest baseline to 95.8%. On the 3D-ZeF zebrafish benchmark, it achieves 81.1% MOTA, compared with 77.4% for the best geometric baseline. TrackFish3D also generalizes beyond fish, achieving strong results on real-world bird tracking.
Comments: to be published in the 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.38347 [cs.CV]
(or arXiv:2609.38347v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.38347
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Patt Phurtivilai [view email] [v1] Tue, 29 Sep 2026 18:10:09 UTC (14,443 KB)
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