Print Email Facebook Twitter Activity Progress Prediction Title Activity Progress Prediction: Is there progress in video progress prediction methods? Author de Boer, Frans (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Pattern Recognition and Bioinformatics) Contributor van Gemert, J.C. (mentor) Pintea, S. (mentor) Bohmer, Wendelin (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2023-07-18 Abstract In this paper, we investigate the behaviour of current progress prediction methods on the currently used benchmark datasets. We show that the progress prediction methods can fail to extract useful information from visual data on these datasets. Moreover, when the methods fail to extract visual information, memory-based methods adopt a frame-counting strategy when presented with \textsl{full-video} data as input. Additionally, we evaluate all the methods on a synthetic dataset we specifically designed for the progress prediction task. On our synthetic dataset the results show that all the methods can make use of the visual information and outperform the native, non-learning baselines. We conclude that in its current form the task of progress prediction is ill-posed. The learning methods tend to fail to extract useful information from the visual data and instead rely purely on frame counting. Subject Computer VisionActivity Progress PredictionDeep LearningRemaining Surgery Duration To reference this document use: http://resolver.tudelft.nl/uuid:0be69536-5b67-49bd-83f7-120bb93c9b42 Part of collection Student theses Document type master thesis Rights © 2023 Frans de Boer Files PDF MSc_Frans_de_Boer_Activit ... iction.pdf 3.91 MB Close viewer /islandora/object/uuid:0be69536-5b67-49bd-83f7-120bb93c9b42/datastream/OBJ/view