Is there progress in activity progress prediction?
Frans de Boer Jan C. van Gemert Jouke Dijkstra Silvia L. Pintea
Activity progress prediction aims to estimate what percentage of an activity
has been completed. Currently this is done with machine learning approaches,
trained and evaluated on complicated and realistic video datasets. The videos
in these datasets vary drastically in length and appearance. And some of the
activities have unanticipated developments, making activity progression
difficult to estimate. In this work, we examine the results obtained by
existing progress prediction methods on these datasets. We find that current
progress prediction methods seem not to extract useful visual information for
the progress prediction task. Therefore, these methods fail to exceed simple
frame-counting baselines. We design a precisely controlled dataset for activity
progress prediction and on this synthetic dataset we show that the considered
methods can make use of the visual information, when this directly relates to
the progress prediction. We conclude that the progress prediction task is
ill-posed on the currently used real-world datasets. Moreover, to fairly
measure activity progression we advise to consider a, simple but effective,
frame-counting baseline.