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.
A number of previous methods only regards the skill level in a coarse manner: In @cite_18 the level of skill is only split into novice and expert. These works @cite_11 @cite_18 determine skill labels by participants’ previous experience, but not their performance in individual videos. In this work we aim to rank the skill in each video instead of classifying the videos as expert or novice.
@cite_18 @cite_11
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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. This paper presents a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate the problem as pairwise (who's better?) and overall (who's best?) ranking of video collections, using supervised deep ranking. We propose a novel loss function that learns discriminative features when a pair of videos exhibit variance in skill, and learns shared features when a pair of videos exhibit comparable skill levels. Results demonstrate our method is applicable across tasks, with the percentage of correctly ordered pairs of videos ranging from 70 to 83 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 organization of how-to video collections and overall, generic skill determination in video. We are given a set of video clips, each one annotated with an ordered list of actions, such as “walk” then “sit” then “answer phone” extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies. We are given a set of video clips, each one annotated with an ordered list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies.