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SLEAP: A deep learning system for multi-animal pose tracking.

Talmo D Pereira^1,2, Nathaniel Tabris¹, Arie Matsliah¹

¹Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.

|April 5, 2022

Summary

This summary is machine-generated.

Social LEAP Estimates Animal Poses (SLEAP) is a new machine learning system for multi-animal pose tracking. It offers high accuracy and speed for analyzing complex social behaviors in diverse species.

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Area of Science:

Neuroscience and ethology
Computer vision and machine learning

Background:

Understanding animal behavior requires accurate quantification of movement.
Markerless pose estimation advances individual animal tracking.
Multi-animal tracking for social behavior studies presents unique challenges.

Purpose of the Study:

Introduce Social LEAP Estimates Animal Poses (SLEAP), a novel machine learning system for multi-animal pose tracking.
Provide a versatile platform for labeling, training, and inference in animal behavior analysis.
Enable real-time applications for studying social interactions.

Main Methods:

Developed SLEAP, a machine learning system with a GUI, standardized data model, and reproducible configuration.
Incorporated over 30 model architectures, part grouping, and identity tracking approaches.
Evaluated SLEAP on diverse datasets (flies, bees, mice, gerbils) and compared with existing methods.

Main Results:

SLEAP achieves high accuracy in multi-animal pose tracking across various species.
Demonstrated high processing speeds (>800 fps) and low latency (<3.5 ms) at high resolution.
Successfully applied SLEAP for real-time control of animal behavior based on social interactions.

Conclusions:

SLEAP significantly advances multi-animal pose tracking capabilities.
The system's versatility and performance support a wide range of behavioral studies.
SLEAP enables novel real-time applications in animal behavior research.