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.
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
arXiv:2608.30789v1 Announce Type: new
Abstract: Supervised deep learning methods enable the rapid processing of ecological image data, but depend on a costly annotation process. Consequently, trainin...
By Leonard Hockerts, Peter S. Stewart, Sarthak Arora, Tiffany J. Vlaar
The paper discusses how active learning (AL) can alleviate the expert annotation bottleneck in biodiversity monitoring by selecting the most informative samples under a fixed budget. It highlights that while AL reduces labeling effort, its non-random sample selection complicates model validation, calibration, and ecological inference, issues often overlooked in current studies. The authors review existing AL research across acoustic and image data, identify gaps such as limited species coverage and lack of real-world deployments, and propose a tutorial framework and roadmap for developing AL methods that support efficient training, reliable validation, and trustworthy ecological conclusions.
By Ben McEwen, Shiqi Zhang, Dan Stowell
arXiv:2606. 27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater platforms, and other sensing systems.
By Brinnae Bent, Holly R. Houliston, Jiayi Zhou, G\"unel Aghakishiyeva, David W. Johnston
arXiv:2609. 11916v1 Announce Type: new Abstract: Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) -- not frontier-scale ones -- the practically relevant class to evaluate for species identification.
By William Zhou, Mayukha Siripuram, Xiao Yan, Ziqi Liu, Yi Ding
The paper presents MaxBoxCount, the winning solution to the iWildCam 2021 Challenge, which tackles counting animals in camera‑trap image sequences without using count labels. It combines a robust species classification pipeline with a counting heuristic based on MegaDetector detections to estimate the number of unique individuals across short image bursts. The method addresses challenges posed by temporal discontinuities and the high cost of manual count annotations.
By Fagner Cunha, Juan G. Colonna, Eulanda M. dos Santos
arXiv:2609.22897v1 Announce Type: cross
Abstract: Large vision-language models (VLMs) enable recognition beyond a fixed class set, but their computational demands prevent them from running on many ed...
By Mohammad Mehdi Rastikerdar, Hui Guan, Deepak Ganesan
PuTR-CouT is a transformer‑based counting‑by‑tracking framework designed for camera‑trap image sequences. It generates synthetic training data using structural priors to create pseudo‑tracking labels, enabling the tracker to associate detections across frames and estimate per‑species counts. The method improves upon the MaxBoxCount baseline on the iWildCam 2021 benchmark, offering competitive counting results along with multi‑species predictions and track‑level verification.
By Fagner Cunha, Juan G. Colonna, Eulanda M. dos Santos
arXiv:2606. 24986v1 Announce Type: new Abstract: Automated cattle posture-classification systems frequently report near-perfect accuracy, yet their robustness under realistic deployment conditions remains largely unknown.
By Leutrim Uka, Severino Pinto, Gundula Hoffmann, Marina M. -C. H\"ohne
arXiv:2606. 09353v1 Announce Type: cross Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms.
By Maria De Marsico, Anil K. Jain, Annalaura Miglino
Det‑LIME is a detector‑aware, multi‑instance adaptation of LIME designed to explain black‑box object detectors used in marine mammal research. It generates instance‑specific, box‑aligned explanations by weighting detections, applying a proximity kernel, and using IoU‑based matching to track instances across perturbations. Evaluated on aerial drone imagery of harbor seals and a seabird case study, Det‑LIME outperformed vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient‑based methods in Attribution Ratio and Max Saliency Hit Rate, offering higher‑resolution, instance‑aware explanations that aid debugging, data augmentation, and modeling improvements.
By Jiayi Zhou, David W. Johnston, Brinnae Bent