Tracktor: Image‐based automated tracking of animal movement and behaviour

Sridhar, Vivek Hari; Roche, Dominique G.; Gingins, Simon

doi:10.1111/2041-210x.13166

Cited by 94 publications

(80 citation statements)

References 17 publications

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“…ROIs with multiple candidates are assigned by minimizing frame-toframe changes in position. Degradation of difference image quality over time (due changes in the background, noisy imaging, and physical perturbation of the imaging setup) constitutes a significant barrier to long term tracking 15 . To address this problem, MARGO continuously monitors the quality of the difference image and updates or reacquires the background image when imaging becomes noisy.…”

Section: Margo Workflowmentioning

confidence: 99%

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

Werkhoven

Rohrsen

Qin

et al. 2019

Preprint

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Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for multiple animals in parallel. We developed MARGO, a real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) robustness, 2) high throughput, 3) flexible control of hardware and 4) real-time closed-loop control of sensory and optogenetic stimuli, all of which are optimized for large-scale experimentation.

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Section: Margo Workflowmentioning

confidence: 99%

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

Werkhoven

Rohrsen

Qin

et al. 2019

Preprint

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“…However, although complex social behavior has been the focus of extensive research for over a century, technological advances are only beginning to enable systematic and simultaneous measurements of behavior in large groups of interacting individuals. Solutions for automated video tracking of insects in social groups can be roughly divided into two categories (for reviews see [5,6]): methods for tracking unmarked individuals [13][14][15][16][17][18][19][20][21], and methods for tracking marked individuals [22,23]. The former category has the obvious advantages of reduced interference with natural behavior, less constrained experimental conditions, and an unbounded number of tracked individuals.…”

Section: Introductionmentioning

confidence: 99%

“…The challenge in this approach is to resolve individuals from each other and link their locations in consecutive frames during close range interactions, when they are touching or occluding each other. Common solutions to this problem are to employ sophisticated segmentation methods [13,14,16], to use predictive modeling of the animal motion [13,20], or to use image characteristics to match individuals before and after occlusions [19]. The success of these solutions is case-specific and will usually be limited to relatively simple problems, where interactions are brief, occlusion is minimal, and image resolution is sufficient to resolve the individuals even during an interaction.…”

Section: Introductionmentioning

confidence: 99%

anTraX: high throughput video tracking of color-tagged insects

Gal

Saragosti

Kronauer

2020

Preprint

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Recent years have seen a surge in methods to track and analyze animal behavior. Nevertheless, tracking individuals in closely interacting, group-living organisms remains a challenge. Here we present anTraX, an algorithm and software package for high-throughput video tracking of color-tagged insects. anTraX combines neural network classification of animals with a novel approach for representing tracking data as a graph, enabling individual tracking even in cases where it is difficult to segment animals from one another, or where tags are obscured. The use of color tags, a well-established and robust method for marking individual insects in groups, relaxes requirements for image size and quality, and makes the software broadly applicable. anTraX is readily integrated into existing tools and methods for automated image analysis of behavior to further augment its output. anTraX can handle large-scale experiments with minimal human involvement, allowing researchers to simultaneously monitor many social groups over long time periods.

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“…organisms, habitats, organs, etc.) and the isolation of these features from other elements in the image (Harvey & Cappo 2001;Mathiassen et al 2011;Matai et al 2012;Li et al 2015;Piechaud et al 2019); and secondly, through automation of object tracking in image sequences, i.e., following the movement of features (Xie, Kham & Shah;Dell et al 2014;Sridhar, Roche & Gingins 2019). These two computer vision tasks promise to reduce the amount of time spent processing image data, so easing the bottleneck created by collecting far more data than can be processed time-effectively.…”

Section: Introductionmentioning

confidence: 99%

A computer vision approach for studying fossorial and cryptic crabs

Herrera

Baker

Sheaves

et al. 2020

Preprint

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Despite the increasing need to catalogue and describe biodiversity and the ecosystem processes it underpins, these tasks remain inherently challenging. This is particularly true for species that are difficult to observe in their natural environment, such as fossorial and cryptic crabs that inhabit intertidal sediments. Traditional sampling techniques for intertidal crabs are often invasive, labour intensive and/or inconsistent. These factors can limit the amount and type of data that can be collected which in turn hinders our ability to obtain reliable ecological estimates and compare findings between studies. Computer vision and machine learning algorithms present an opportunity to innovate and improve sampling approaches. Moreover, cheaper and tougher recording devices and the diversity of open source software further boost the possibilities of achieving rigorous image-based sampling, which can broaden the range of questions that can be addressed from the data collected. Despite its significant potential, the software and algorithms associated with image-based sampling may be daunting to researchers without expertise in computer vision. Therefore, there is a need to develop protocols and data processing workflows to showcase the value of embracing new technologies. This paper presents a non-invasive computer vision and learning protocol for sampling fossorial and cryptic crabs in their natural environment. The image-based protocol is underpinned by fit-for-purpose and off-the-shelf software. We demonstrate this approach using fiddler crab and sediment recordings to study and quantify crab abundance, motion patterns, behaviour, intraspecific interactions, and estimate bioturbation rates. We discuss current limitations in this protocol and identify opportunities for improvement and additional data stream options that can be obtained from this approach. We conclude that this protocol can overcome some of the limitations associated with traditional techniques for sampling intertidal crabs, and could be applied to other taxa or ecosystems that present similar challenges. We believe this sampling and analytical framework represents an important step forward in understanding the ecology of species and their functional role within ecosystems.

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Tracktor: Image‐based automated tracking of animal movement and behaviour

Cited by 94 publications

References 17 publications

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

anTraX: high throughput video tracking of color-tagged insects

A computer vision approach for studying fossorial and cryptic crabs

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