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UBC Theses and Dissertations

Active exploration of training data for improved object detection Okuma, Kenji


This thesis concerns the problem of object detection, which is defined as finding all instances of an object class of interest and fitting each of them with a tight bounding window. This seemingly easy task for humans is still extremely difficult for machines. However, recent advances in object detection have enabled machines to categorize many classes of objects. Statistical models are often used for representing an object class of interest. These models learn from extensive training sets and generalize with low error rates to unseen data in a highly generic manner. But, these statistical methods have a major drawback in that they require a large amount of training data. We approach this problem by making the process of acquiring labels less tedious and less costly by reducing human labelling effort. Throughout this thesis, we explore means of efficient label acquisition for realizing cheaper training, faster development time, and higher-performance of object detectors. We use active learning with our novel interface to combine machine intelligence with human interventions, and effectively improve a state-of-the-art classifier by using additional unlabelled images from the Web. As the approach relies on a small amount of label input from a human oracle, there is still room to further reduce the amount of human effort. An ideal solution is, if possible, to have no humans involved in labelling novel data. Given a sparsely labelled video that contains very few labels, our novel self-learning approach achieves automatic acquisition of additional labels from the unlabelled portion of the video. Our approach combines colour segmentation, object detection and tracking in order to discover potential labels from novel data. We empirically show that our self-learning approach improves the performance of models that detect players in broadcast footage of sports games.

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