Birds are important indicators for monitoring both biodiversity and habitat
health; they also play a crucial role in ecosystem management. Decline in bird
populations can result in reduced eco-system services, including seed
dispersal, pollination and pest control. Accurate and long-term monitoring of
birds to identify species of concern while measuring the success of
conservation interventions is essential for ecologists. However, monitoring is
time consuming, costly and often difficult to manage over long durations and at
meaningfully large spatial scales. Technology such as camera traps, acoustic
monitors and drones provide methods for non-invasive monitoring. There are two
main problems with using camera traps for monitoring: a) cameras generate many
images, making it difficult to process and analyse the data in a timely manner;
and b) the high proportion of false positives hinders the processing and
analysis for reporting. In this paper, we outline an approach for overcoming
these issues by utilising deep learning for real-time classi-fication of bird
species and automated removal of false positives in camera trap data. Images
are classified in real-time using a Faster-RCNN architecture. Images are
transmitted over 3/4G cam-eras and processed using Graphical Processing Units
(GPUs) to provide conservationists with key detection metrics therefore
removing the requirement for manual observations. Our models achieved an
average sensitivity of 88.79%, a specificity of 98.16% and accuracy of 96.71%.
This demonstrates the effectiveness of using deep learning for automatic bird
monitoring.

本文介绍了一种通过利用深度学习技术实现鸟类在野外自动监测的方法，基于摄像机捕捉数据，使用 Faster-RCNN 架构实时分类图像，并通过 GPU 进一步处理数据，从而有效地减少了手动观测的需要，并取得了较为显著的监测鸟类的准确率

利用摄像机陷阱图像和深度学习消除在鸟类分类中的人为瓶颈

Removing Human Bottlenecks in Bird Classification Using Camera Trap  Images and Deep Learning

Animals are diverse in shape, but building a deformable shape model for a new
species is not always possible due to the lack of 3D data. We present a method
to capture new species using an articulated template and images of that
species. In this work, we focus mainly on birds. Although birds represent
almost twice the number of species as mammals, no accurate shape model is
available. To capture a novel species, we first fit the articulated template to
each training sample. By disentangling pose and shape, we learn a shape space
that captures variation both among species and within each species from image
evidence. We learn models of multiple species from the CUB dataset, and
contribute new species-specific and multi-species shape models that are useful
for downstream reconstruction tasks. Using a low-dimensional embedding, we show
that our learned 3D shape space better reflects the phylogenetic relationships
among birds than learned perceptual features.

本文介绍了一种使用关节模板和物种的图像来捕捉新物种的方法，重点关注鸟类。通过解开姿态和形状，从图像证据中学习捕捉各物种和每个物种内部变化的形状空间，并使用低维嵌入，展示了所学 3D 形状空间比感知特征更好地反映了鸟类之间的系统关系，并提供了一些新的物种特定和多物种形状模型，有利于下游重建任务。