A deep active learning system for species identification and counting in camera trap images

@article{Norouzzadeh2019ADA,
  title={A deep active learning system for species identification and counting in camera trap images},
  author={Mohammad Sadegh Norouzzadeh and Dan Morris and Sara Beery and Neel Joshi and Nebojsa Jojic and Jeff Clune},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.09716}
}
Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and non-intrusive. However, extracting useful information from camera trap images is a cumbersome process: a typical camera trap survey may produce millions of images that require slow, expensive manual review. Consequently, critical information is often lost due to… Expand
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References

SHOWING 1-10 OF 58 REFERENCES
Deep Learning Object Detection Methods for Ecological Camera Trap Data
TLDR
This work demonstrates the capabilities of two deep learning object detection classifiers, Faster R-CNN and YOLO v2.0, to identify, quantify, and localize animal species within camera trap images using the Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data sets. Expand
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
TLDR
The ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences is investigated. Expand
Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
TLDR
This work deployed 225 camera traps across Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics and classified the images via the citizen-science website www.snapshotsereNGeti.org, yielding a final classification for each image and a measure of agreement among individual answers. Expand
Insights and approaches using deep learning to classify wildlife
TLDR
Light is shed on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications of wildlife species from camera-trap data, and presents dataset biases that were revealed by these extracted features. Expand
REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes
TLDR
Evaluating the consistency of CT protocols and sampling designs, the extent to which CT surveys considered sampling error, and the linkages between analytical assumptions and species ecology call for more explicit consideration of underlying processes of animal abundance, movement and detection by cameras, including more thorough reporting of methodological details and assumptions. Expand
Recognition in Terra Incognita
It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. WeExpand
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TLDR
This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features. Expand
Deep Residual Learning for Image Recognition
TLDR
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. Expand
Active Learning for Convolutional Neural Networks: A Core-Set Approach
TLDR
This work defines the problem of active learning as core-set selection as choosing set of points such that a model learned over the selected subset is competitive for the remaining data points, and presents a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. Expand
Camera traps in animal ecology : methods and analyses
1. Introduction: Allan F. O'Connell, James D. Nichols, and K. Ullas Karanth.- 2. A History of Camera Trapping: Thomas E. Kucera and Reginald H. Barrett.- 3. Evaluating Types and Features of CameraExpand
...
1
2
3
4
5
...