Is there progress in activity progress prediction?

IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 2950–2958

Abstract

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.

Authors 4

1. Frans de Boer

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology,Computer Vision Lab

   Computer Vision Lab, Delft University of Technology

2. Jan C. van Gemert

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology,Computer Vision Lab

   Computer Vision Lab, Delft University of Technology

3. Jouke Dijkstra Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center,Division of Image Processing (LKEB)

   Division of Image Processing (LKEB), Leiden University Medical Center

4. Silvia L. Pintea Aachen Division of Image Processing (LKEB)

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology,Computer Vision Lab

   Computer Vision Lab, Delft University of Technology

   Division of Image Processing (LKEB), Leiden University Medical Center