arXiv Computer Vision
CamAgent: An LLM-Agent Framework for Multi-Species Camera-Trap Workflows
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arXiv AI
Jul 14
Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data
arXiv:2607. 09876v1 Announce Type: cross Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging.
arXiv AI
Jun 29
Explainable AI for Biodiversity Monitoring and Ecological Image Analysis
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.
arXiv Computer Vision
4d ago
DeepForestVisionV2: Ecology-Driven Taxonomy Expansion for Camera-Trap Monitoring in African Tropical Forests
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...
arXiv AI
Jun 10
Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals
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.
arXiv Computer Vision
Sep 7
PuTR-CouT: Counting-by-Tracking in Camera-Trap Image Sequences
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.
arXiv Computer Vision
Aug 24
WildFin: An In-the-Wild Dataset for Fish Behavioral Recognition
arXiv:2608.21281v1 Announce Type: new Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging...