Improved 3D tracking and automated classification of rodents’ behavioral activity using depth-sensing cameras

Gerós, Ana; Magalhães, Ana; Aguiar, Paulo

doi:10.3758/s13428-020-01381-9

Cited by 21 publications

(13 citation statements)

References 39 publications

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Section: Datasetmentioning

confidence: 99%

“…An open-access RGB-D behavioral dataset, available at https://doi.org/10.5281/zenodo.3636135 10 , was used for all experiments. Details on the experimental procedures, video acquisition and manual annotation of rodent's behavior can be found in 10 . In brief, the dataset is composed of 10 to 15 min RGB-D video sequences of individual Wistar rat behavior, recorded with a Microsoft Kinect v2 camera (512x424 depth pixel resolution).…”

Section: Datasetmentioning

confidence: 99%

“…Besides reducing human bias and subjectivity, and consequently allowing for the standardization of measurements across laboratories, behavioral patterns that were once unnoticed to a human observer may now be explored at different scales and resolutions [4][5][6] . The first approaches to successfully combine computer vision and machine learning techniques typically relied on hand-crafted features extracted from images or video sequences that can be then used for automated behavior classification using supervised [7][8][9][10] or unsupervised [11][12][13] learning methods. However, such approaches are highly dependent on domain expertise for feature engineering, often losing their generalization capability in the presence of a new environment/scenario.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing

Gerós

Cruz

Chaumont

et al. 2022

Preprint

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Robust quantification of animal behavior is fundamental in experimental neuroscience research. Systems providing automated behavioral assessment are an important alternative to manual measurements avoiding problems such as human bias, low reproducibility and high cost. Integrating these tools with closed-loop control systems creates conditions to correlate environment and behavioral expressions effectively, and ultimately explain the neural foundations of behavior. We present an integrated solution for automated behavioral analysis of rodents using deep learning networks on video streams acquired from a depth-sensing camera. The use of depth sensors has notable advantages: tracking/classification performance is improved and independent of animals' coat color, and videos can be recorded in dark conditions without affecting animals' natural behavior. Convolutional and recurrent layers were combined in deep network architectures, and both spatial and temporal representations were successfully learned for a 4-classes behavior classification task (standstill, walking, rearing and grooming). Integration with Arduino microcontrollers creates an easy-to-use control platform providing low-latency feedback signals based on the deep learning automatic classification of animal behavior. The complete system, combining depth-sensor camera, computer, and Arduino microcontroller, allows simple mapping of input-output control signals using the animal's current behavior and position. For example, a feeder can be controlled not by pressing a lever but by the animal behavior itself. An integrated graphical user interface completes a user-friendly and cost-effective solution for animal tracking and behavior classification. This open-software/open-hardware platform can boost the development of customized protocols for automated behavioral research, and support ever more sophisticated, reliable and reproducible behavioral neuroscience experiments.

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Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing

Gerós

Cruz

Chaumont

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Second, the change rate of intensity or colors can be directly used to follow objects (e.g., a Kalman filter or particle filter). [15][16][17][18][19][20] Besides algorithms for two-dimensional (2D) tracking, there are methods for three-dimensional (3D) tracking of fish, [21][22][23][24][25][26] rodents, 27 dolphins, 28 and flies. 29 While 3D tracking can provide additional insights for behavioral analysis, 2D tracking is often preferred for its simplicity in experiment design and evaluation, and should therefore not be omitted.…”

Section: Introductionmentioning

confidence: 99%

“…Besides algorithms for two-dimensional (2D) tracking, there are methods for three-dimensional (3D) tracking of fish, 21–26 rodents, 27 dolphins, 28 and flies. 29 While 3D tracking can provide additional insights for behavioral analysis, 2D tracking is often preferred for its simplicity in experiment design and evaluation, and should therefore not be omitted.…”

Section: Introductionmentioning

confidence: 99%