idTracker: tracking individuals in a group by automatic identification of unmarked animals - PubMed (original) (raw)
doi: 10.1038/nmeth.2994. Epub 2014 Jun 1.
Affiliations
- PMID: 24880877
- DOI: 10.1038/nmeth.2994
idTracker: tracking individuals in a group by automatic identification of unmarked animals
Alfonso Pérez-Escudero et al. Nat Methods. 2014 Jul.
Abstract
Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).
Comment in
- Distinguishing seemingly indistinguishable animals with computer vision.
Branson K. Branson K. Nat Methods. 2014 Jul;11(7):721-2. doi: 10.1038/nmeth.3004. Nat Methods. 2014. PMID: 24972171 No abstract available.
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