相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Three critical factors affecting automated image species recognition performance for camera traps
Stefan Schneider et al.
ECOLOGY AND EVOLUTION (2020)
Applications for deep learning in ecology
Sylvain Christin et al.
METHODS IN ECOLOGY AND EVOLUTION (2019)
Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry
Patrick C. Gray et al.
METHODS IN ECOLOGY AND EVOLUTION (2019)
A convolutional neural network for detecting sea turtles in drone imagery
Patrick C. Gray et al.
METHODS IN ECOLOGY AND EVOLUTION (2019)
Past, present and future approaches using computer vision for animal re-identification from camera trap data
Stefan Schneider et al.
METHODS IN ECOLOGY AND EVOLUTION (2019)
Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data
Shoaib Ahmed Siddiqui et al.
ICES JOURNAL OF MARINE SCIENCE (2018)
A computer vision for animal ecology
Ben G. Weinstein
JOURNAL OF ANIMAL ECOLOGY (2018)
Scene-specific convolutional neural networks for video-based biodiversity detection
Ben G. Weinstein
METHODS IN ECOLOGY AND EVOLUTION (2018)
Multi-modal survey of Adelie penguin mega-colonies reveals the Danger Islands as a seabird hotspot
Alex Borowicz et al.
SCIENTIFIC REPORTS (2018)
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)
A survey of deep neural network architectures and their applications
Weibo Liu et al.
NEUROCOMPUTING (2017)
Group foraging increases foraging efficiency in a piscivorous diver, the African penguin
Alistair M. McInnes et al.
ROYAL SOCIETY OPEN SCIENCE (2017)
Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks
Alexander Gomez Villa et al.
ECOLOGICAL INFORMATICS (2017)
ECO: Efficient Convolution Operators for Tracking
Martin Danelljan et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Animal Detection From Highly Cluttered Natural Scenes Using Spatiotemporal Object Region Proposals and Patch Verification
Zhi Zhang et al.
IEEE TRANSACTIONS ON MULTIMEDIA (2016)
SMALL UNMANNED AERIAL VEHICLES (MICRO-UAVS, DRONES) IN PLANT ECOLOGY
Mitchell B. Cruzan et al.
APPLICATIONS IN PLANT SCIENCES (2016)
MotionMeerkat: integrating motion video detection and ecological monitoring
Ben G. Weinstein
METHODS IN ECOLOGY AND EVOLUTION (2015)
Learning Spatiotemporal Features with 3D Convolutional Networks
Du Tran et al.
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)
A Novel Method to Reduce Time Investment When Processing Videos from Camera Trap Studies
Kristijn R. R. Swinnen et al.
PLOS ONE (2014)
Lightweight unmanned aerial vehicles will revolutionize spatial ecology
Karen Anderson et al.
FRONTIERS IN ECOLOGY AND THE ENVIRONMENT (2013)
Programmable, miniature video-loggers for deployment on wild birds and other wildlife
Christian Rutz et al.
METHODS IN ECOLOGY AND EVOLUTION (2013)
Penguin-mounted cameras glimpse underwater group behaviour
A Takahashi et al.
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES (2004)