The iWildCam 2018 Challenge Dataset

Abstract

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it is imperative that we develop automatic solutions for annotating camera trap data in order to allow this research to scale. A promising approach is based on deep networks trained on human-annotated images. We provide a challenge dataset to explore whether such solutions generalize to novel locations, since systems that are trained once and may be deployed to operate automatically in new locations would be most useful.

Challenge hosted at the fifth Fine-Grained Visual Categorization Workshop (FGVC5) at CVPR 2018

References in corpus (1)

• Camera-trap images segmentation using multi-layer robust principal component analysis

Cited by in corpus (12)

• An explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining
• ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification
• The iWildCam 2021 Competition Dataset
• The Fishnet Open Images Database: A Dataset for Fish Detection and Fine-Grained Categorization in Fisheries
• Low-power, Continuous Remote Behavioral Localization with Event Cameras
• The iWildCam 2019 Challenge Dataset
• Florida Wildlife Camera Trap Dataset
• The iWildCam 2020 Competition Dataset
• Efficient Method for Categorize Animals in the Wild
• Image-to-Image Translation of Synthetic Samples for Rare Classes
• Domain Adaptation for Rare Classes Augmented with Synthetic Samples
• Unifying data for fine-grained visual species classification