733 citations · 1.3k across the 29 of their papers we have counts for
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Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
Sara Beery, Guanhang Wu, Vivek Rathod +2
In static monitoring cameras, useful contextual information can stretch far beyond the few seconds typical video understanding models might see: subjects may exhibit similar behavi…
A deep active learning system for species identification and counting in camera trap images
Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery +3
Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical…
The iWildCam 2019 Challenge Dataset
Sara Beery, Dan Morris, Pietro Perona
Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population…
Efficient Pipeline for Camera Trap Image Review
Sara Beery, Dan Morris, Siyu Yang
Biologists all over the world use camera traps to monitor biodiversity and wildlife population density. The computer vision community has been making strides towards automating the…
Synthetic Examples Improve Generalization for Rare Classes
Sara Beery, Yang Liu, Dan Morris +5
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detectin…
The iWildCam 2018 Challenge Dataset
Sara Beery, Grant van Horn, Oisin Mac Aodha +1
Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation. With the vast amounts of data now available it…