Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification

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Recognition in Terra Incognita (2018, methodology): “[26] uses several statistical background-modeling approaches as additional signal to improve and speed up deep detection.”
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].”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, background): “[397] designed a joint background modeling and deep learning classification to detect and distinguish human and animals.”
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.”