anTraX, a software package for high-throughput video tracking of color-tagged insects

Gal, A.; Saragosti, Jonathan; Kronauer, Daniel J. C.

doi:10.7554/elife.58145

Cited by 43 publications

(48 citation statements)

References 76 publications

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“…BACH's detection performance also fell when multiple ants engaged in clusters (‘double’ and ‘double plus one’ detections), because telling apart overlapping ant shapes became difficult. A recently developed tracking system (AnTrax [ 13 , 32 ]) has elegantly worked around this issue via retaining the identities of individuals when they join a cluster and become indistinguishable from other ants. When such individuals leave the cluster, AnTrax retrieves their identity.…”

Section: Discussionmentioning

confidence: 99%

“…More recent 'next generation' video tracking systems integrate insect identification with analyses of their shapes and movements [13]. This not only allows to automatically identify individual animals, but also to classify some of their behaviours [13,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers usually employ tracking systems to analyse groups of ants moving in two-dimensional arenas, tagging each individual with unique identity markers (e.g. QR or ArUco matrix codes [ 7 , 8 , 11 , 12 ] or combinations of painted colour dots [ 4 – 6 , 13 ]) and using cameras to take images from top and/or bottom [ 14 ]. Many video trackers rely on inferring the position and orientation of insects via the detection of individual tags in images taken at given time intervals [ 7 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Combined together, tag positions, relative code orientations and distances allow reconstructing social interaction networks within groups [ 3 , 15 , 16 ]. More recent ‘next generation’ video tracking systems integrate insect identification with analyses of their shapes and movements [ 13 ]. This not only allows to automatically identify individual animals, but also to classify some of their behaviours [ 13 , 16 – 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Integrating real-time data analysis into automatic tracking of social insects

et al. 2021

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Automatic video tracking has become a standard tool for investigating the social behaviour of insects. The recent integration of computer vision in tracking technologies will probably lead to fully automated behavioural pattern classification within the next few years. However, many current systems rely on offline data analysis and use computationally expensive techniques to track pre-recorded videos. To address this gap, we developed BACH (Behaviour Analysis maCHine), a software that performs video tracking of insect groups in real time. BACH uses object recognition via convolutional neural networks and identifies individually tagged insects via an existing matrix code recognition algorithm. We compared the tracking performances of BACH and a human observer (HO) across a series of short videos of ants moving in a two-dimensional arena. We found that BACH detected ant shapes only slightly worse than the HO. However, its matrix code-mediated identification of individual ants only attained human-comparable levels when ants moved relatively slowly, and fell when ants walked relatively fast. This happened because BACH had a relatively low efficiency in detecting matrix codes in blurry images of ants walking at high speeds. BACH needs to undergo hardware and software adjustments to overcome its present limits. Nevertheless, our study emphasizes the possibility of, and the need for, further integrating real-time data analysis into the study of animal behaviour. This will accelerate data generation, visualization and sharing, opening possibilities for conducting fully remote collaborative experiments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating real-time data analysis into automatic tracking of social insects

et al. 2021

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“…extreme occlusion/overlap between individuals, as occurs regularly in studies on social insects (Gal, Saragosti & Kronauer, 2020).…”

Section: Machine Learning-based Segmentation Detection and Trackingmentioning

confidence: 92%

scAnt—an open-source platform for the creation of 3D models of arthropods (and other small objects)

Plum¹,

Labonte²

2021

PeerJ

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We present scAnt, an open-source platform for the creation of digital 3D models of arthropods and small objects. scAnt consists of a scanner and a Graphical User Interface, and enables the automated generation of Extended Depth Of Field images from multiple perspectives. These images are then masked with a novel automatic routine which combines random forest-based edge-detection, adaptive thresholding and connected component labelling. The masked images can then be processed further with a photogrammetry software package of choice, including open-source options such as Meshroom, to create high-quality, textured 3D models. We demonstrate how these 3D models can be rigged to enable realistic digital specimen posing, and introduce a novel simple yet effective method to include semi-realistic representations of approximately planar and transparent structures such as wings. As a result of the exclusive reliance on generic hardware components, rapid prototyping and open-source software, scAnt costs only a fraction of available comparable systems. The resulting accessibility of scAnt will (i) drive the development of novel and powerful methods for machine learning-driven behavioural studies, leveraging synthetic data; (ii) increase accuracy in comparative morphometric studies as well as extend the available parameter space with area and volume measurements; (iii) inspire novel forms of outreach; and (iv) aid in the digitisation efforts currently underway in several major natural history collections.

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