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 and
can be indicators of ocean health. 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 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 dynamic
environments characterized by significant variation and the need for
geo-awareness. The code and settings described in this paper can be found on
our website: https://lfk118.github.io/ZooplanktonBench_Webpage.
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