Real time lobster posture estimation for behavior research

Sheng Yan; Jo Arve Alfredsen

doi:10.1117/12.2266430

8 February 2017 Real time lobster posture estimation for behavior research

Sheng Yan, Jo Arve Alfredsen

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102250F (2017) https://doi.org/10.1117/12.2266430
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

In animal behavior research, the main task of observing the behavior of an animal is usually done manually. The measurement of the trajectory of an animal and its real-time posture description is often omitted due to the lack of automatic computer vision tools. Even though there are many publications for pose estimation, few are efficient enough to apply in real-time or can be used without the machine learning algorithm to train a classifier from mass samples. In this paper, we propose a novel strategy for the real-time lobster posture estimation to overcome those difficulties. In our proposed algorithm, we use the Gaussian mixture model (GMM) for lobster segmentation. Then the posture estimation is based on the distance transform and skeleton calculated from the segmentation. We tested the algorithm on a serials lobster videos in different size and lighting conditions. The results show that our proposed algorithm is efficient and robust under various conditions.

Citation Download Citation

Sheng Yan and Jo Arve Alfredsen "Real time lobster posture estimation for behavior research", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250F (8 February 2017); https://doi.org/10.1117/12.2266430

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KEYWORDS

Image segmentation

RGB color model

Video

Machine learning

Algorithm development

Analytical research

Computer vision technology

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