Kavli Affiliate: Fukun Liu
| First 5 Authors: Fukun Liu, Adam T. Greer, Gengchen Mai, Jin Sun,
| Summary:
Plankton are small drifting organisms found throughout the world’s oceans.
One component of this plankton community is the zooplankton, which includes
gelatinous animals and crustaceans (e.g. shrimp), as well as the early life
stages (i.e., eggs and larvae) of many commercially important fishes. Being
able to monitor zooplankton abundances accurately and understand how
populations change in relation to ocean conditions is invaluable to marine
science research, with important implications for future marine seafood
productivity. While new imaging technologies generate massive amounts of video
data of zooplankton, analyzing them using general-purpose computer vision tools
developed for general objects turns out to be highly challenging due to the
high similarity in appearance between the zooplankton and its background (e.g.,
marine snow). In this work, we present the ZooplanktonBench, a benchmark
dataset containing images and videos of zooplankton associated with rich
geospatial metadata (e.g., geographic coordinates, depth, etc.) in various
water ecosystems. ZooplanktonBench defines a collection of tasks to detect,
classify, and track zooplankton in challenging settings, including highly
cluttered environments, living vs non-living classification, objects with
similar shapes, and relatively small objects. Our dataset presents unique
challenges and opportunities for state-of-the-art computer vision systems to
evolve and improve visual understanding in a dynamic environment with huge
variations and be geo-aware.
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