Weakly Supervised Faster-RCNN+FPN to classify animals in camera trap images

TL;DR

This paper introduces a weakly supervised object detection workflow using Faster-RCNN+FPN to classify animals in camera trap images, requiring only image-level labels and leveraging motion cues for bounding box annotation.

Contribution

It presents a novel weakly supervised approach that automatically generates bounding boxes from motion cues, enabling effective animal classification without manual annotations.

Findings

Effective animal classification on camera trap images.

Reduced need for manual bounding box annotations.

Validated on datasets from Papua New Guinea and Missouri.

Abstract

Camera traps have revolutionized the animal research of many species that were previously nearly impossible to observe due to their habitat or behavior. They are cameras generally fixed to a tree that take a short sequence of images when triggered. Deep learning has the potential to overcome the workload to automate image classification according to taxon or empty images. However, a standard deep neural network classifier fails because animals often represent a small portion of the high-definition images. That is why we propose a workflow named Weakly Object Detection Faster-RCNN+FPN which suits this challenge. The model is weakly supervised because it requires only the animal taxon label per image but doesn't require any manual bounding box annotations. First, it automatically performs the weakly-supervised bounding box annotation using the motion from multiple frames. Then, it trains a Faster-RCNN+FPN model using this weak supervision. Experimental results have been obtained with two datasets from a Papua New Guinea and Missouri biodiversity monitoring campaign, then on an easily reproducible testbed.