Recent advances in computer vision have made it possible to automatically assess from videos the manipulation skills of humans in performing a task, which breeds many important applications in domains such as health rehabilitation and manufacturing. Previous methods of video-based skill assessment did not consider the attention mechanism humans use in assessing videos, limiting their performance as only a small part of video regions is informative for skill assessment. Our motivation here is to estimate attention in videos that helps to focus on critically important video regions for better skill assessment. In particular, we propose a novel RNN-based spatial attention model that considers accumulated attention state from previous frames as well as high-level knowledge about the progress of an undergoing task. We evaluate our approach on a newly collected dataset of infant grasping task and four existing datasets of hand manipulation tasks. Experiment results demonstrate that state-of-the-art performance can be achieved by considering attention in automatic skill assessment.
This paper presents a method for assessing skill of performance from video, for a variety of tasks, ranging from drawing to surgery and rolling dough. We formulate the problem as pairwise and overall ranking of video collections, and propose a supervised deep ranking model to learn discriminative features between pairs of videos exhibiting different amounts of skill. We utilise a two-stream Temporal Segment Network to capture both the type and quality of motions and the evolving task state. Results demonstrate our method is applicable to a variety of tasks, with the percentage of correctly ordered pairs of videos ranging from 70 to 82 for four datasets. We demonstrate the robustness of our approach via sensitivity analysis of its parameters. We see this work as effort toward the automated and objective organisation of how-to videos and overall, generic skill determination in video.