arXiv:2606. 10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles.
By Paul Fergus, Philip Stephens, Russell A. Hill, Lee Oliver, Katie Appleby, Sarah Beatham, Naomi Davies Walsh, Stuart Nixon, Naomi Matthews, Chris Sutherland, Kelly Hitchcock
Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles. In an attempt to remove barriers and increase uptake, we release an open-source object detection model for 31 classes, 28 common UK mammal and bird species, plus utility classes for humans, calibration poles, and vehicles, drawn from a curated dataset of 48,165 labelled instances assembled from multiple sites over a decade of operational deployment through Conservation AI and its successor, Trap Tracker.
BMCTrack-d is a novel tracking-by-detection method that uses unique back marks on pigs to achieve robust re-identification and tracking in challenging side-view camera settings. The approach employs a neural network-based back mark classifier followed by temporal consistency checks and deduplication to improve identity reliability over time. On a demanding test set, BMCTrack-d outperforms strong baselines, achieving higher-order tracking accuracy gains of 9.11% and 1.03%.
By David Brunner, Maciej Oczak, Marie Bordes, Jean-Loup Rault, Stephan M. Winkler, Viktoria Dorfer
arXiv:2606.20223v2 Announce Type: replace
Abstract: Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Am...
By Hugo Magaldi, Theau d'Audiffret, Etienne Francois Akomo-Okoue, Bala Amarasekaran, Naomi Anderson, Claire Auger, Noemie Cappelle, Daniel Cornelis, Raphael Cornette, Tobias Deschner, Gabriel Dubus, Davy Fonteyn, Rosa M. Garriga, Jennifer Hatlauf, Innocent Kasekendi, Raymond Katumba, Aram Kazandjian, Alfred Ngomanda, Stephan Ntie, Simone Pika, Xavier Rufray, Harold Rugonge, John Justice Tibesigwa, Peter van Lunteren, Hadrien Vanthomme, Joeri A. Zwerts, Sabrina Krief
BMCTrack-d is a novel tracking‑by‑detection system that uses unique back marks on pigs to enable reliable re‑identification and tracking in challenging side‑view camera settings. The approach combines a neural network back‑mark classifier with two post‑processing stages—temporal prediction consistency checks and deduplication—to improve identity reliability over time. On a demanding test set, BMCTrack‑d outperforms strong baselines (BoT‑SORT‑ReID and TrackTrack‑ReID) by 9.11% and 1.03% in higher‑order tracking accuracy, demonstrating the effectiveness of back‑mark‑based re‑identification for individual‑level pig monitoring.
arXiv:2608.28663v1 Announce Type: new
Abstract: Reliable individual cattle identification supports disease surveillance, vaccination records, breeding management, and livestock insurance. Although th...
By Lalit BC, Dharmendra Singh Chaudhary, Shovit Nepal