Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks

By Alexander Gómez-Villa, Augusto Salazar, Jesús Francisco Vargas-Bonilla. 2016

335 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ecology, Molecular Biology

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Citations in the corpus

Recognition in Terra Incognita (2018, neutral): “[27] show classification results on both Snapshot Serengeti and data from jungles in Panama, and saw a boost in classification performance from providing animal segmentations.”
The iWildCam 2019 Challenge Dataset (2019, background): “Automating camera trap labeling is not a new challenge for the computer vision community [1, 3–10, 12–17].”
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning (2017, methodology): “Because we want to compare our results to theirs and since the exact dataset used in (7) is not publicly available, we did our best to reproduce it by including all images from those 26 classes.”
Automatically identifying wild animals in camera trap images with deep learning (2017, methodology): “This modiﬁed dataset contains only ∼ 33,000 images and the images were manually cropped and speciﬁcally chosen to have animals in the foreground (32).”
Efficient Pipeline for Camera Trap Image Review (2019, methodology): “The computer vision community has been making strides towards automating the species classification challenge in camera traps [1, 2, 4–16], but it has proven difficult to to apply models trained in one region to images collected in different geographic areas.”
Identifying animal species in camera trap images using deep learning and citizen science (2018, methodology): “More recent studies in automatically identifying animal species using CNNs have reported accuracies around 90% with some manual preprocessing (Gomez Villa et al., 2017) or more complex pipelines involving auto - matic preprocessing (Giraldo-Zuluaga, Salazar, Gomez, & Diaz-Pulido, 2017).”