TY - JOUR
T1 - Covfee
T2 - ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on Computer Vision, ICCV 2021
AU - Vargas-Quiros, Jose
AU - Tan, Stephanie
AU - Raman, Chirag
AU - Cabrera-Quiros, Laura
AU - Hung, Hayley
N1 - Publisher Copyright:
© 2022 J. Vargas-Quiros, S. Tan, C. Raman, L. Cabrera-Quiros & H. Hung.
PY - 2021
Y1 - 2021
N2 - Continuous-time annotation, where subjects annotate data while watching the continuous media (video, audio, or time series in general) has traditionally been applied to the annotation of continuous-value variables like arousal and valence in Affective Computing. On the other hand, machine perception tasks are most often annotated using frame-wise techniques. For actions, annotators find the start and end frame of the action of interest using a graphical interface. However, given the duration of the videos that are generally annotated in social interaction datasets, this can be a slow and frustrating process. It usually involves pausing the video at the onset or offset of the action and scrolling back and forth to identify the precise moment. A continuous annotation system, where annotators are asked to press a key when they perceive the target action to be occurring, can improve the time to do such annotations, especially in situations where single subjects are annotated for long periods of time. Keypoint annotations, where the task is to follow a particular point of interest in a video (e.g., a body joint) can also be done continuously. In this paper we present the Covfee web framework, a software package designed to support online continuous annotation tasks, with crowd-sourcing capabilities. We present results from case studies of continuous annotation of body poses (keypoints) and speaking (action) on an in-the-wild social interaction dataset. In the case of keypoints, we present a new technique allowing an easy way to follow a keypoint in a video using the mouse cursor. We found the technique to significantly reduce annotation times with no adverse effect on inter-annotator agreement. For action annotation, we used continuous annotation techniques to obtain binary speaking status labels and annotator ratings of confidence on those labels. Covfee is free software, available as a Python package documented at josedvq.github.io/covfee.
AB - Continuous-time annotation, where subjects annotate data while watching the continuous media (video, audio, or time series in general) has traditionally been applied to the annotation of continuous-value variables like arousal and valence in Affective Computing. On the other hand, machine perception tasks are most often annotated using frame-wise techniques. For actions, annotators find the start and end frame of the action of interest using a graphical interface. However, given the duration of the videos that are generally annotated in social interaction datasets, this can be a slow and frustrating process. It usually involves pausing the video at the onset or offset of the action and scrolling back and forth to identify the precise moment. A continuous annotation system, where annotators are asked to press a key when they perceive the target action to be occurring, can improve the time to do such annotations, especially in situations where single subjects are annotated for long periods of time. Keypoint annotations, where the task is to follow a particular point of interest in a video (e.g., a body joint) can also be done continuously. In this paper we present the Covfee web framework, a software package designed to support online continuous annotation tasks, with crowd-sourcing capabilities. We present results from case studies of continuous annotation of body poses (keypoints) and speaking (action) on an in-the-wild social interaction dataset. In the case of keypoints, we present a new technique allowing an easy way to follow a keypoint in a video using the mouse cursor. We found the technique to significantly reduce annotation times with no adverse effect on inter-annotator agreement. For action annotation, we used continuous annotation techniques to obtain binary speaking status labels and annotator ratings of confidence on those labels. Covfee is free software, available as a Python package documented at josedvq.github.io/covfee.
KW - action annotation
KW - annotation tool
KW - continuous annotation
KW - crowd-sourcing
KW - human behavior annotation
KW - pose annotation
UR - http://www.scopus.com/inward/record.url?scp=85142381515&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85142381515
SN - 2640-3498
VL - 173
SP - 265
EP - 293
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
Y2 - 16 October 2021
ER -