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    Where's Wally? A Machine Learning Approach

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    Date
    2021
    Author
    Barthelmes, Tobias
    Vidal, Franck ORCID
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    Abstract
    Object detection has been implemented in all sorts of real-life scenarios such as facial recognition, traffic monitoring and medical imaging but the research that has gone into object detection in drawings and cartoons is not nearly as extensive. The Where's Wally puzzle books give a good opportunity to implement some of these real-life methods into the fictional world. The Wally detection framework proposed is composed of two stages: i) a Haar-cascade classifier based on the Viola-Jones framework, which detects possible candidates from a scenario from the Where'sWally books, and ii) a lightweight convolutional neural network (CNN) that re-labels the objects detected by the cascade classifier. The cascade classifier was trained on 85 positive images and 172 negative images. It was then applied to 12 test images, which produced over 400 false positives. To increase the accuracy of the models, hard negative mining was implemented. The framework achieved a recall score of 84.61% and an F1 score of 78.54%. Improvements could be made to the training data or the CNN to further increase these scores.
    BibTeX
    @inproceedings {10.2312:cgvc.20211313,
    booktitle = {Computer Graphics and Visual Computing (CGVC)},
    editor = {Xu, Kai and Turner, Martin},
    title = {{Where's Wally? A Machine Learning Approach}},
    author = {Barthelmes, Tobias and Vidal, Franck},
    year = {2021},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-158-8},
    DOI = {10.2312/cgvc.20211313}
    }
    URI
    https://doi.org/10.2312/cgvc.20211313
    https://diglib.eg.org:443/handle/10.2312/cgvc20211313
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    • Computer Graphics & Visual Computing (CGVC) 2021

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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
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