Finding areas of motion in camera trap images

23 citations recorded by the source corpus.

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Recognition in Terra Incognita (2018, methodology): “[24] creates background models and perform a superpixel-based comparison to determine areas of motion.”
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.”
Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification (2017, methodology): “[3] proposes a method for constructing a background model using the median pixel value in dynamic scenes.”
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].”