2,388 research outputs found
Automatic coral reef fish identification and 3D measurement in the wild
In this paper we present a pipeline using stereo images in order to
automatically identify, track in 3D fish, and measure fish population.Comment: This paper is in its draft version and should be improved in order to
be published. This paper is issued from one Year of Engineering wor
Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second
To study the behavior of freely moving model organisms such as zebrafish
(Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it
would be ideal to use a light microscope that can resolve 3D information over a
wide field of view (FOV) at high speed and high spatial resolution. However, it
is challenging to design an optical instrument to achieve all of these
properties simultaneously. Existing techniques for large-FOV microscopic
imaging and for 3D image measurement typically require many sequential image
snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a
computational microscope based on a synchronized array of 54 cameras that can
capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to
230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second.
3D-RAPID features a 3D reconstruction algorithm that, for each synchronized
temporal snapshot, simultaneously fuses all 54 images seamlessly into a
globally-consistent composite that includes a coregistered 3D height map. The
self-supervised 3D reconstruction algorithm itself trains a
spatiotemporally-compressed convolutional neural network (CNN) that maps raw
photometric images to 3D topography, using stereo overlap redundancy and
ray-propagation physics as the only supervision mechanism. As a result, our
end-to-end 3D reconstruction algorithm is robust to generalization errors and
scales to arbitrarily long videos from arbitrarily sized camera arrays. The
scalable hardware and software design of 3D-RAPID addresses a longstanding
problem in the field of behavioral imaging, enabling parallelized 3D
observation of large collections of freely moving organisms at high
spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex
barbatus), fruit flies, and zebrafish larvae
Webcams for Bird Detection and Monitoring: A Demonstration Study
Better insights into bird migration can be a tool for assessing the spread of avian borne infections or ecological/climatologic issues reflected in deviating migration patterns. This paper evaluates whether low budget permanent cameras such as webcams can offer a valuable contribution to the reporting of migratory birds. An experimental design was set up to study the detection capability using objects of different size, color and velocity. The results of the experiment revealed the minimum size, maximum velocity and contrast of the objects required for detection by a standard webcam. Furthermore, a modular processing scheme was proposed to track and follow migratory birds in webcam recordings. Techniques such as motion detection by background subtraction, stereo vision and lens distortion were combined to form the foundation of the bird tracking algorithm. Additional research to integrate webcam networks, however, is needed and future research should enforce the potential of the processing scheme by exploring and testing alternatives of each individual module or processing step
Semi-Supervised Visual Tracking of Marine Animals using Autonomous Underwater Vehicles
In-situ visual observations of marine organisms is crucial to developing
behavioural understandings and their relations to their surrounding ecosystem.
Typically, these observations are collected via divers, tags, and
remotely-operated or human-piloted vehicles. Recently, however, autonomous
underwater vehicles equipped with cameras and embedded computers with GPU
capabilities are being developed for a variety of applications, and in
particular, can be used to supplement these existing data collection mechanisms
where human operation or tags are more difficult. Existing approaches have
focused on using fully-supervised tracking methods, but labelled data for many
underwater species are severely lacking. Semi-supervised trackers may offer
alternative tracking solutions because they require less data than
fully-supervised counterparts. However, because there are not existing
realistic underwater tracking datasets, the performance of semi-supervised
tracking algorithms in the marine domain is not well understood. To better
evaluate their performance and utility, in this paper we provide (1) a novel
dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2)
an evaluation of state-of-the-art semi-supervised algorithms in the context of
underwater animal tracking, and (3) an evaluation of real-world performance
through demonstrations using a semi-supervised algorithm on-board an autonomous
underwater vehicle to track marine animals in the wild.Comment: To appear in IJCV SI: Animal Trackin
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