FISHnet: Learning to Segment the Silhouettes of Swimmers

ASCENSO, Guido, YAP, Moi Hoon, ALLEN, Thomas Bruce, CHOPPIN, Simon S. and PAYTON, Carl (2020). FISHnet: Learning to Segment the Silhouettes of Swimmers. IEEE Access, 8, 178311-178321.

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Official URL: https://ieeexplore.ieee.org/document/9206530
Open Access URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&ar... (Published version)
Link to published version:: https://doi.org/10.1109/access.2020.3027260
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    Abstract

    We present a novel silhouette extraction algorithm designed for the binary segmentation of swimmers underwater. The intended use of this algorithm is within a 2D-to-3D pipeline for the markerless motion capture of swimmers, a task which has not been achieved satisfactorily, partly due to the absence of silhouette extraction methods that work well on images of swimmers. Our algorithm, FISHnet, was trained on the novel Scylla dataset, which contains 3,100 images (and corresponding hand-traced silhouettes) of swimmers underwater, and achieved a dice score of 0.9712 on its test data. Our algorithm uses a U-Net-like architecture and VGG16 as a backbone. It introduces two novel modules: a modified version of the Semantic Embedding Branch module from ExFuse, which increases the complexity of the features learned by the layers of the encoder; and the Spatial Resolution Enhancer module, which increases the spatial resolution of the features of the decoder before they are skip connected with the features of the encoder. The contribution of these two modules to the performance of our network was marginal, and we attribute this result to the lack of data on which our network was trained. Nevertheless, our model outperformed state-of-the-art silhouette extraction algorithms (namely DeepLabv3+) on Scylla, and it is the first algorithm developed specifically for the task of accurately segmenting the silhouettes of swimmers.

    Item Type: Article
    Uncontrolled Keywords: 08 Information and Computing Sciences; 09 Engineering; 10 Technology
    Identification Number: https://doi.org/10.1109/access.2020.3027260
    Page Range: 178311-178321
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 08 Oct 2020 11:37
    Last Modified: 08 Oct 2020 11:45
    URI: http://shura.shu.ac.uk/id/eprint/27374

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