3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset

Pedersen, Malte; Haurum, Joakim Bruslund; Bengtson, Stefan Hein; Moeslund, Thomas B.

doi:10.1109/cvpr42600.2020.00250

Cited by 36 publications

(18 citation statements)

References 63 publications

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“…However, tracking zebrafish in three dimensions (3D) has proven difficult [29]. To the best of our knowledge, previous studies on the 3D locomotion of zebrafish focussed either on the development of the methodology [30,31], or were limited to very small group sizes (N � 5) [29,32,33], while ideally one would like to study the 3D behaviour of a statistically significant number of individuals, representative of a typical community. In the field, zebrafish swim in 3D with group sizes ranging from tens to thousands [34].…”

Section: Introductionmentioning

confidence: 99%

Dominating lengthscales of zebrafish collective behaviour

et al. 2022

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Collective behaviour in living systems is observed across many scales, from bacteria to insects, to fish shoals. Zebrafish have emerged as a model system amenable to laboratory study. Here we report a three-dimensional study of the collective dynamics of fifty zebrafish. We observed the emergence of collective behaviour changing between ordered to randomised, upon adaptation to new environmental conditions. We quantify the spatial and temporal correlation functions of the fish and identify two length scales, the persistence length and the nearest neighbour distance, that capture the essence of the behavioural changes. The ratio of the two length scales correlates robustly with the polarisation of collective motion that we explain with a reductionist model of self–propelled particles with alignment interactions.

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Section: Introductionmentioning

confidence: 99%

Dominating lengthscales of zebrafish collective behaviour

et al. 2022

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“…AI-driven analyses, especially based on artificial neural networks (ANNs), are becoming popular methods in animal behavioral analyses, including pioneering AI studies of zebrafish drug-induced behavior [34]. ANNs have also analyzed tracks of zebrafish in two [52] and three dimensions [53], as well as in larval fish [54]. More recently, unsupervised deep learning systems successfully linked zebrafish social behavior to dopamine D3 receptor agonism [55].…”

Section: Discussionmentioning

confidence: 99%

MPTP-Treated Zebrafish Recapitulate ‘Late-Stage’ Parkinson’s-like Cognitive Decline

Bashirzade

Чересиз

Belova

et al. 2022

Toxics

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The zebrafish is a promising model species in biomedical research, including neurotoxicology and neuroactive drug screening. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) evokes degeneration of dopaminergic neurons and is commonly used to model Parkinson’s disease (PD) in laboratory animals, including zebrafish. However, cognitive phenotypes in MPTP-evoked experimental PD models remain poorly understood. Here, we established an LD50 (292 mg/kg) for intraperitoneal MPTP administration in adult zebrafish, and report impaired spatial working memory (poorer spontaneous alternation in the Y-maze) in a PD model utilizing fish treated with 200 µg of this agent. In addition to conventional behavioral analyses, we also employed artificial intelligence (AI)-based approaches to independently and without bias characterize MPTP effects on zebrafish behavior during the Y-maze test. These analyses yielded a distinct cluster for 200-μg MPTP (vs. other) groups, suggesting that high-dose MPTP produced distinct, computationally detectable patterns of zebrafish swimming. Collectively, these findings support MPTP treatment in adult zebrafish as a late-stage experimental PD model with overt cognitive phenotypes.

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“…A more sophisticated approach was proposed in [12], which combines multi-view Bayesian network modeling of occlusion relationship and homography correspondence, across all views, with height-adaptive projection (HAP) to obtain final ground plane detections [12]. Stereobased MOT approaches have also demonstrated improved 3D object estimation and tracking [33], [34], [35].…”

Section: Related Workmentioning

confidence: 99%

“…Z(•) is a matrix-to-vector transformation that transforms the conic into a 4D bounding box (in the same format as z (c) ). The illustration of the overall transformation (35) is depicted in Fig. 5.…”

Section: Object Representation and Model Parametersmentioning

confidence: 99%

A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling

Ong

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections across the cameras, and hence scales gracefully with the number of cameras. It operates in the 3D world frame, and provides 3D trajectory estimates of the objects. The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusion/misdetection handling. The proposed algorithm is evaluated on the latest WILDTRACKS dataset, and demonstrated to work in very crowded scenes on a new dataset.

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3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset

Cited by 36 publications

References 63 publications

Dominating lengthscales of zebrafish collective behaviour

Dominating lengthscales of zebrafish collective behaviour

MPTP-Treated Zebrafish Recapitulate ‘Late-Stage’ Parkinson’s-like Cognitive Decline

A Bayesian Filter for Multi-View 3D Multi-Object Tracking With Occlusion Handling

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