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NAPS: Integrating pose estimation and tag-based tracking
Scott W Wolf1, Dee M Ruttenberg1, Daniel Y Knapp2
1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
Methods in Ecology and Evolution
|April 29, 2024
Summary
Computational ethology advances allow detailed behavior tracking. NAPS (ArUco Plus SLEAP) is a new hybrid framework combining pose estimation and identity tracking for social animals like bumblebees.
Area of Science:
- Computational ethology
- Animal behavior analysis
- Social network dynamics
Background:
- Quantifying animal behavior has advanced significantly with computational ethology.
- Tracking individuals within social groups remains a challenge, often sacrificing pose detail for identity retention, or vice versa.
Purpose of the Study:
- To develop a hybrid tracking framework, NAPS (ArUco Plus SLEAP), that accurately captures fine-grained behaviors while maintaining individual identity in social groups.
- To enable detailed investigation of social dynamics and individual behavioral variation within a group setting.
Main Methods:
- NAPS integrates deep learning-based pose estimation (SLEAP) with unique markers (ArUco) for robust identity persistence.
- The framework was applied to study the social dynamics of the common eastern bumblebee (*Bombus impatiens*).
Main Results:
- NAPS successfully captures finely resolved behaviors and maintains individual identity over time.
- The framework scales to long-duration, high-frame-rate experiments, facilitating detailed behavioral variation analysis within groups.
- Demonstrated application in analyzing the social dynamics of *Bombus impatiens*.
Conclusions:
- NAPS provides a crucial tool for advancing the study of social group behavior and network dynamics.
- This framework enables the collection of critical data for understanding how individual behavioral variations influence collective dynamics in social species.
