Camera traps are crucial in biodiversity motivated studies, however dealing
with large number of images while annotating these data sets is a tedious and
time consuming task. To speed up this process, Machine Learning approaches are
a reasonable asset. In this article we are proposing an easy, accessible,
light-weight, fast and efficient approach based on our winning submission to
the "Hakuna Ma-data - Serengeti Wildlife Identification challenge". Our system
achieved an Accuracy of 97% and outperformed the human level performance. We
show that, given relatively large data sets, it is effective to look at each
image only once with little or no augmentation. By utilizing such a simple, yet
effective baseline we were able to avoid over-fitting without extensive
regularization techniques and to train a top scoring system on a very limited
hardware featuring single GPU (1080Ti) despite the large training set (6.7M
images and 6TB).

本文介绍了基于机器学习的相机陷阱图像批注方法。我们的方法通过只查看每个图像一次，不使用增量方法，依靠相对较大的数据集，在单个 GPU 上训练了一个高分系统，从而获得 97% 的准确率，并超过了人类的表现水平。

高效掌握大规模多标签图像识别在相机陷阱图像中的应用

Mastering Large Scale Multi-label Image Recognition with high efficiency  overCamera trap images

Non intrusive monitoring of animals in the wild is possible using camera
trapping framework, which uses cameras triggered by sensors to take a burst of
images of animals in their habitat. However camera trapping framework produces
a high volume of data (in the order on thousands or millions of images), which
must be analyzed by a human expert. In this work, a method for animal species
identification in the wild using very deep convolutional neural networks is
presented. Multiple versions of the Snapshot Serengeti dataset were used in
order to probe the ability of the method to cope with different challenges that
camera-trap images demand. The method reached 88.9% of accuracy in Top-1 and
98.1% in Top-5 in the evaluation set using a residual network topology. Also,
the results show that the proposed method outperforms previous approximations
and proves that recognition in camera-trap images can be automated.

使用深度卷积神经网络在野外通过摄像监控自动识别动物种类的方法，优于之前的尝试，证明了在摄像监控的图像中，识别可以自动化。