Automated image-based tracking and its application in ecology.
By Anthony I. Dell, John A. Bender, Kristin Branson, Iain D. Couzin, G. D. de Polavieja, Lucas P. J. J. Noldus, A. Pérez-Escudero, Pietro Perona, Andrew D. Straw, Martin Wikelski and 1 more. Trends in Ecology & Evolution, 2014
487 citations recorded by the source corpus.
Subfields: Ecological Modeling, Ecology
View Paper: https://www.semanticscholar.org/paper/0006fb029fb64c639301d419faf856d9c440d63a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecological Modeling
- E. Mächler
- Michael E. Schaepman
- Ben. G. Weinstein
- Duccio Rocchini
- Andrew K. Skidmore
- Marco Heurich
- Martin Wikelski
- Josef Settele
In Ecology
- E. Mächler
- Andrew K. Skidmore
- Michael E. Schaepman
- Ben. G. Weinstein
- Duccio Rocchini
- Marco Heurich
- Martin Wikelski
- Josef Settele
Citations in the corpus
- Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives (2016, methodology): “…the use of tereo and light-field cameras, newly-developed giga-pixel cam- esolution natural colour images, showing red deer (b,c) in Bavarian Forest National era technologies as well as the latest technology for slow-motion cameras to record animal tracking in 3D and 4D modes (Dell et al., 2014).”
- BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion (2015, methodology): “In ethology, automated tracking techniques have allowed for the collection of vast, highly-accurate behavioral datasets [6 – 8], which can be used, for example, in detailed quantitative analysis of animal behavior [9,10].”
- Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks (2014, background): “…from remote-sensing imagery can help to answer pressing questions such as why certain migratory bird numbers are decreasing, why certain species start to migrate earlier or later in the year, or why resting or breeding habitats are given up (Dell et al. 2014; Cooke et al. 2004).”
- A computer vision for animal ecology. (2018, methodology): “Applications from specimen morphometrics, microscopy (Pennekamp & Schtickzelle, 2013) and animal tracking in laboratory settings are reviewed elsewhere (Dell et al., 2014; Robie, Seagraves, Egnor, & Branson, 2017).”
- Applications of machine learning in animal behaviour studies (2017, unknown): “ML is used to automate the classification of behaviours/activities (Kabra et al., 2013) and tracking movement trajectories (Dell et al., 2014).”
- Big answers from small worlds: a user's guide for protist microcosms as a model system in ecology and evolution (2015, methodology): “Digital image and video analysis can provide this information (Pennekamp & Schtickzelle 2013;Dell et al. 2014).”