Deep convolutional neural network based species recognition for wild animal monitoring
By Guobin Chen, Tony X. Han, Zhihai He, R. Kays, Tavis D. Forrester. 2014
177 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Plant Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrea Vedaldi
- Trevor Darrell
- Dacheng Tao
- Jeff Clune
- Alan L. Yuille
- Xiaogang Wang
- Sara Beery
In Plant Science
Citations in the corpus
- BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain (2017, background): “Convolutional neural networks (CNNs) in particular have been wildly successful for image processing tasks, and CNN-based image recognition models have been deployed to help identify plant and animal species [8] and autonomously drive cars [9].”
- Recognition in Terra Incognita (2018, methodology): “[19] were the first to take a deep network approach to camera trap classification, working with data from eMammal [40].”
- 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.”
- The iWildCam 2019 Challenge Dataset (2019, unknown): “(4) Occlusion (5) Camouflage (6) Perspective”
- Automatically identifying wild animals in camera trap images with deep learning (2017, methodology): “Interestingly, Chen et al. found that DNNs outperforms a traditional Bag of Words technique (29, 30) if provided sufficient training data (27).”
- Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks (2016, methodology): “In order to compare with previous approaches, the camera-trap dataset used in [5] was also used (this dataset will be referenced as Panama dataset in the following).”
- Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning (2017, methodology): “However, they demonstrated the techniques on a dataset of ∼20,000 images and 20 classes, which is of much smaller scale than we explore here (30).”