TIGER ID is exploring whether the stripe patterns across a tiger’s back and shoulders can support individual identification from aerial images, at scale and without disturbing the animal.
A research question in progress. Aerial tiger identification has not yet been validated.
Most established tiger re-identification uses side views. TIGER ID puts the overhead view at the centre and tests the back, shoulders and upper body as a distinct pattern signature.
We need to learn whether those patterns remain distinctive as a tiger moves, turns, is partly hidden, or appears under different cameras, heights and light. The answer must come from images with independently verified identities.
Use the regions that are visible in each image. A partial view can still contribute evidence; no single region should be treated as decisive on its own.
Main dorsal stripe field for overhead comparison.
High-value markings around the shoulder blades.
Extra evidence when the face and crown are clear.
Additional stripe geometry along the rear body.
Supplementary markings when visible at useful detail.
Use what already exists to establish a baseline, then test aerial image handling, and finally answer the question that matters for TIGER ID.
Run RAPID on ATRW, a labelled Amur tiger benchmark captured mainly from ground-level views. Record the same split and metrics so the result is reproducible.
Ground-view baselineUse AerialCattle2017 to examine how RAPID behaves on top-down patterned-animal images. This helps test viewpoint and image preparation, but it is not a tiger test.
Aerial pipeline checkCompare aerial tiger images with known identities across separate sightings. First secure a suitable labelled dataset; without it, aerial-tiger accuracy cannot be measured.
Core TIGER ID testSimilarity is evidence for a human reviewer, not a verdict. Every result should make room for “we don’t know yet.”
Use only when a pre-set threshold and independent evidence support the match. Preserve the supporting images and review trail.
Show the closest candidate when evidence is suggestive but incomplete. Ask a qualified reviewer to assess it.
Return no identification when images are poor, views do not align, or the best candidate is not convincing.
RAPID gives TIGER ID a credible baseline for patterned-animal matching and edge deployment. Its published tiger result does not demonstrate aerial tiger identification.
View RAPID on GitHub ↗It matches cropped patterned-animal images to a reference database, returns candidate IDs with confidence scores, and is designed to run on CPU and edge devices. It also includes tools for checking potentially incorrect ground-truth labels.
The published RAPID evaluation reports a 95% top-1 result on ATRW, an Amur tiger benchmark. The benchmark images are mixed ground views; this result is not aerial tiger accuracy.
RAPID was also evaluated on AerialCattle2017, a nadir drone dataset of cattle. That supports exploring an overhead workflow, but cattle results cannot establish tiger performance.
The RAPID repository notes that successful re-identification benefits from reference images with a similar viewpoint. An aerial-to-ground tiger match is therefore a separate research challenge.
Repository: robot-perception-group/RAPID-animal-reidentification
Environment check HEAD: 7402a550c4ebf2d0480bc9e0c7e6f9e031236b85 — verified as the August 2026 README update.
Zábó et al. (2026), “RAPID: Real-time animal pattern re-identification on edge devices, an open-source tool for field deployment.” Read the paper ↗ · ATRW dataset ↗
Selected decisions and excerpts are kept here as reference material, so the research direction and why it changed remain visible.
The project shifted toward mass identification from drones. Ground images remain supplementary context; the system’s primary question is whether dorsal patterns can identify a tiger from above.
“Can we identify individual wild tigers at scale, using aerial imaging and artificial intelligence, without physically disturbing them?”
The conversation identified a key difference between flank photos and drone views. The research should treat side, overhead and oblique images as different categories and test whether identities can bridge them.
“If a drone were taking them it would likely be from above.”
The five-region plan combines back, shoulders, head, hindquarters and tail. Regions contribute when visible, with the back and shoulders as the core aerial signature.
“The back and shoulders should be treated as primary identification regions.”
Three outcomes were agreed: identified, possible match and unknown. A low-quality or unfamiliar view should be allowed to return no answer; human review remains part of the design.
The project explicitly calls for an “unknown” result when evidence is insufficient.
The research plan separates the ATRW tiger benchmark, the overhead cattle pipeline check, and the future aerial-tiger experiment. Only the final stage directly tests the central TIGER ID hypothesis.
“Our experiment must establish whether its methods transfer to overhead imagery.”
The drone remains the main route for scale. Small autonomous crawlers and solar observation stations are possible supporting ideas for equipment checks and repeat observations; they are not part of the core identification claim.
“Drones for identification, solar stations for continuous observation, and tiny crawlers as optional mobile support.”
A related field-support idea in the project chats. It does not replace the drone-led TIGER ID research.
Small autonomous crawlers might help check equipment or collect environmental information along existing tracks. The stronger near-term supporting idea may be fixed solar observation stations, where canopy and charging conditions allow.
Reproduce the RAPID baseline, document the image-view gap, then pursue verified aerial tiger data.