Where's Wally? A Machine Learning Approach

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dc.contributor.authorBarthelmes, Tobiasen_US
dc.contributor.authorVidal, Francken_US
dc.contributor.editorXu, Kai and Turner, Martinen_US
dc.date.accessioned2021-09-07T05:44:59Z
dc.date.available2021-09-07T05:44:59Z
dc.date.issued2021
dc.description.abstractObject 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.en_US
dc.description.sectionheadersComputer Vision
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20211313
dc.identifier.isbn978-3-03868-158-8
dc.identifier.pages27-31
dc.identifier.urihttps://doi.org/10.2312/cgvc.20211313
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20211313
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectApplied computing
dc.subjectMedia arts
dc.titleWhere's Wally? A Machine Learning Approachen_US
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