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Real-time rodent behavior classifier using color-based body segmentation (R2C2)

Lab Animal volume 54, pages 321–334 (2025)Cite this article

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Abstract

The heavy reliance of animal studies on human observers makes them prone to observer-specific biases. To mitigate such shortcomings, much research has been focused on computerizing the characterization of animal behavior. Such automation can lead to more reliable and cost-effective behavior quantifications. Yet, there remain challenges in developing end-to-end solutions that allow users to easily train custom behavioral classifiers with minimal data while maintaining low computational demands. Here we resolve these challenges through a rodent behavior classifier, the real-time rodent behavior classifier using color-based body segmentation (R2C2) algorithm, which uses color-based body segmentation to track rodent body parts and consequently their behaviors. Based on the ‘hue, saturation, value’ (HSV) color difference in furs or exposed skins, the R2C2 creates simple white–black color boundaries for each body part, which are then used to discern and track body parts in real time to extract movement-based features. We combined wavelet transform-based tracking with HSV color-based body part segmentation to substantially reduce computational requirements while minimizing the number of input features needed for classification. Loading these features into our convolutional neural network algorithm, the R2C2 achieves performance on par with an expert human observer. Furthermore, it can differentiate subtle behavioral patterns associated with autism spectrum disorder in mouse models. As the R2C2 is a complete, lightweight end-to-end pipeline package with a graphical user interface and does not require end-user programming or heavy computation resources, it can be easily adopted in conventional neuroscience laboratories. By enabling effective auto-labeling of fine animal actions, R2C2 will facilitate studies aiming to uncover the neural mechanisms driving behavioral modulations.

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Fig. 1: Overview of R2C2.
Fig. 2: Operational sequence of the R2C2 method.
Fig. 3: Body part segmentation by HSV color space.
Fig. 4: Robustness of R2C2 tracking in various environments.
Fig. 5: Features extraction from R2C2 tracking.
Fig. 6: Identification of distinct behavioral patterns by primary and wavelet features.
Fig. 7: R2C2 performance for behavior classification.
Fig. 8: Comparison of structures with grouped behavioral motifs.
Fig. 9: Analyzing distribution to verify hidden features of natural behaviors.
Fig. 10: Latency of all steps in the R2C2.

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Data availability

The data that support the findings of this study are available from the corresponding authors upon request. Source data are provided with this paper.

Code availability

Executable codes, GUI software, user manual and input data with sample video are available via GitHub at https://github.com/KIST-BSI/R2C2.git. To help users who wish to make modifications to the source code, the attached ‘README.md’ file provides detailed guidance on the code structure.

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Acknowledgements

We thank H. S. Choi for the discussion and valuable comments and G. Feng for providing SHANK3 TG mice. This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (grant nos. 2019M3E5D2A01058329, RS-2023-00208692, RS-2024-00398768 and RS-2024-00444714) and by the Korea Institute of Science and Technology Institutional Programs (grant nos. 2E29222, 2E33711, 2E33681 and 2E33731).

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Author notes

  1. These authors contributed equally: Jung Ah Jo, Taeyun Park.

Authors and Affiliations

  1. Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea

    Jung Ah Jo, Suhyun Hwang, Jihwan Min, Geunhong Park, Changhyuk Lee & Jeongjin Kim

  2. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

    Jung Ah Jo

  3. Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea

    Taeyun Park

  4. Institute of Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea

    Taeyun Park & Hwiyoung Kim

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Sunwoo Lee

  6. Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark

    Minho Song

  7. Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea

    Jeehyun Kwag

  8. Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hwiyoung Kim

  9. Division of Bio-Medical Science and Technology, University of Science and Technology, Daejeon, Republic of Korea

    Jeongjin Kim

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Contributions

J. Kim and C.L. conceived and supervised all aspects of the study. J. Kim, C.L., J.A.J. and T.P. contributed to algorithm development and designed the experiments. J.A.J., T.P., S.H., S.L. and J.M. performed experiments and analyzed and interpreted the data. G.P. performed animal behavioral labeling and statistical analysis. M.S., J. Kwag and H.K. provided technical consultation for behavioral classification and computation tools and supervised deep learning and model implementation. All authors participated in writing or reviewing paper and approved the final paper.

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Correspondence to Changhyuk Lee or Jeongjin Kim.

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Lab Animal thanks Alvaro Rodriguez, Eric Yttri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–9 and Table 1.

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Supplementary Video 1 (download MP4 )

Representative video illustrating the R2C2 GUI workflow. The video demonstrates defining HSV ranges for ‘body’ and ‘ear’ binary masks, extracting primary and wavelet features and performing behavior classification using feature files and 1D-CNN models.

Supplementary Video 2 (download MP4 )

Representative video of body and ear tracking in R2C2. Trajectories indicate the body center (ROI centroid) and ear centroid.

Supplementary Video 3 (download MP4 )

Representative video of behavioral categories. The video shows four labeled behaviors—grooming, rearing, walking and sniffing—with all others grouped as ‘Other’. This categorization was used to train the 1D-CNN model.

Source data

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Jo, J.A., Park, T., Hwang, S. et al. Real-time rodent behavior classifier using color-based body segmentation (R2C2). Lab Anim 54, 321–334 (2025). https://doi.org/10.1038/s41684-025-01634-0

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