The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

Kay, Justin; Stathatos, Suzanne; Deng, Siqi; Young, E.; Beery, Sara; Horn, Grant Van; Perona, Pietro

doi:10.1007/978-3-031-20074-8_17

Cited by 17 publications

(18 citation statements)

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“…Given the rapid and efficient transmission of sounds in water, active acoustic monitoring has almost exclusively been carried out in aquatic environments using sonar technologies (Figure 2 ). The acoustic features present in sonar echo data can be used to automatically detect small organisms like copepods and krill (Bernard & Steinberg, 2013 ), and track fish and squids at depths over 800 m (Dunlop et al, 2018 ; Kay et al, 2022 ). The taxonomic resolution of sonar remains low, and these technologies are unable to classify most organisms at the species level (Benoit‐Bird & Lawson, 2016 ).…”

Section: From Automated Data Collection To Ecological Knowledgementioning

confidence: 99%

Towards the fully automated monitoring of ecological communities

et al. 2022

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Ecosystems are increasingly exposed to stressors, leading to unprecedented rates of biodiversity decline globally (Capdevila et al., 2022;Díaz et al., 2019). Our ability to reliably forecast ecosystems dynamics is limited by our capacity to understand what governs their composition, dynamics, function and structure (Dietze et al., 2018;Petchey et al., 2015). To drive predictive ecology forward and design appropriate conservation strategies, we therefore need access to long-term, highresolution and standardised information about ecosystems' abiotic and biotic components (Farley et al., 2018;

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Section: From Automated Data Collection To Ecological Knowledgementioning

confidence: 99%

Towards the fully automated monitoring of ecological communities

et al. 2022

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“…The white boxes are the annotated fish swimming through the stream. Images have been provided from the Alaska Department of Fish and Game and Caltech [86]. …”

Section: Discussionmentioning

confidence: 99%

“…Recent advances in hardware have allowed computer vision to expand to underwater locations. The Caltech Fish Counting task leverages sonar cameras placed in rivers to detect, track and count salmon as they swim upstream [ 86 ]. The set-up of these cameras within rivers is illustrated in figure 4 .…”

Section: Discussionmentioning

confidence: 99%

“…Fisheries managers review the videos and manually count the number of salmon. Caltech researchers are working on automating this with computer vision [ 86 ].

Figure 4 Sonar camera arrangement: here are some diagrams of the camera deployment.…”

Section: Discussionmentioning

confidence: 99%

“…Fisheries managers review the videos and manually count the number of salmon. Caltech researchers are working on automating this with computer vision [86].…”

Section: Case Study 3: Computer Visionmentioning

confidence: 99%

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Conservation tools: the next generation of engineering–biology collaborations

Schulz

Shriver

Stathatos

et al. 2023

J. R. Soc. Interface.

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The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that use technology to aid in the conservation of wildlife. In this review, we present five case studies and infer a framework for designing conservation tools (CT) based on human–wildlife interaction. Successful CT range in complexity from cat collars to machine learning and game theory methodologies and do not require technological expertise to contribute to conservation tool creation. Our goal is to introduce researchers to the field of conservation technology and provide references for guiding the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet’s resources.

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Fourier‐Based Action Recognition for Wildlife Behavior Quantification with Event Cameras

Hamann,

Ghosh,

Juárez Martínez

et al. 2024

Advanced Intelligent Systems

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Event cameras are novel bioinspired vision sensors that measure pixel‐wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantages, namely, a high dynamic range, low latency, and minimal motion blur. Modern computer vision algorithms often rely on artificial neural network approaches, which require image‐like representations of the data and cannot fully exploit the characteristics of event data. Herein, approaches to action recognition based on the Fourier transform are proposed. The approaches are intended to recognize oscillating motion patterns commonly present in nature. In particular, the approaches are applied to a recent dataset of breeding penguins annotated for “ecstatic display,” a behavior where the observed penguins flap their wings at a certain frequency. It is found that the approaches are both simple and effective, producing slightly lower results than a deep neural network (DNN) while relying just on a tiny fraction of the parameters compared to the DNN (five orders of magnitude fewer parameters). They work well despite the uncontrolled, diverse data present in the dataset. It is hoped that this work opens a new perspective on event‐based processing and action recognition.

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The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

Cited by 17 publications

References 100 publications

Towards the fully automated monitoring of ecological communities

Towards the fully automated monitoring of ecological communities

Conservation tools: the next generation of engineering–biology collaborations

Fourier‐Based Action Recognition for Wildlife Behavior Quantification with Event Cameras

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