Using physics engines to track objects in images

Chaumont, Fabrice de; Dufour, Alexandre; Serreau, Pierre; Chabout, Jonathan; Munter, S.; Frischknecht, Friedrich; Granon, Sylvie; Olivo-Marin, J.-C.

doi:10.1109/isbi.2009.5193313

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The tracking performance of the proposed unsupervised learning approach was compared to the MiceProfiler [ 14 , 19 ]. For this purpose, the MiceProfiler was carefully configured according to the tutorial provided by the authors.…”

Section: Resultsmentioning

confidence: 99%

“…Instead of the nose, the physics model implemented in the MiceProfiler software keeps track of the head position. We therefore estimated the optimal extension of the straight line from the shoulder to the head position [ 19 ] that minimizes the mean distance to the nose position given in the ground truth. The same strategy was applied for the tail base position by extending the straight line from the belly to the tail position.…”

Section: Resultsmentioning

confidence: 99%

“…The MiceProfiler is a sophisticated software system based on physics engines [19, 20] that has been evaluated comprehensively [14]. Tracking accuracy of the proposed method was validated by computing the Euclidean distances …”

Section: Methodsmentioning

confidence: 99%

“…To compare the performance, the MiceProfiler tracking software [ 14 ] served as benchmark for the proposed method. The MiceProfiler is a sophisticated software system based on physics engines [ 19 , 20 ] that has been evaluated comprehensively [ 14 ]. Tracking accuracy of the proposed method was validated by computing the Euclidean distances …”

Section: Methodsmentioning

confidence: 99%

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An unsupervised learning approach for tracking mice in an enclosed area

et al. 2017

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BackgroundIn neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis.ResultsWe present an unsupervised learning procedure that is basically built as a two-stage process to track mice in an enclosed area using shape matching and deformable segmentation models. The system is validated by comparing the tracking results with previously manually labeled landmarks in three setups with different environment, contrast and lighting conditions. Furthermore, we demonstrate that the system is able to automatically detect non-social and social behavior of interacting mice. The system demonstrates a high level of tracking accuracy and clearly outperforms the MiceProfiler, a recently proposed tracking software, which serves as benchmark for our experiments.ConclusionsThe proposed method shows promising potential to automate behavioral screening of mice and other animals. Therefore, it could substantially increase the experimental throughput in behavioral assessment automation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An unsupervised learning approach for tracking mice in an enclosed area

et al. 2017

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Rule Set Based Joint State Update

Grundmann

Fiegert

Burgard

2012

Springer Tracts in Advanced Robotics

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Computerized video analysis of social interactions in mice

Chaumont¹,

Coura²,

Serreau³

et al. 2012

Nat Methods

193

224

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The study of social interactions in mice is used as a model for normal and pathological cognitive and emotional processes. But extracting comprehensive behavioral information from videos of interacting mice is still a challenge. We describe a computerized method and software, MiceProfiler, that uses geometrical primitives to model and track two mice without requiring any specific tagging. The program monitors a comprehensive repertoire of behavioral states and their temporal evolution, allowing the identification of key elements that trigger social contact. Using MiceProfiler we studied the role of neuronal nicotinic receptors in the establishment of social interactions and risk-prone postures. We found that the duration and type of social interactions with a conspecific evolves differently over time in mice lacking neuronal nicotinic receptors (Chrnb2-/-, here called β2(-/-)), compared to C57BL/6J mice, and identified a new type of coordinated posture, called back-to-back posture, that we rarely observed in β2(-/-) mice.

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Using physics engines to track objects in images

Cited by 4 publications

References 9 publications

An unsupervised learning approach for tracking mice in an enclosed area

An unsupervised learning approach for tracking mice in an enclosed area

Rule Set Based Joint State Update

Computerized video analysis of social interactions in mice

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