Correlating Visual Speaker Gestures with Measures of Audience Engagement to Aid Video Browsing
type: CreativeWork
description: In this thesis, we argue that in the domains of educational lectures and political debates, speaker gestures can be a source of semantic cues for video browsing. We hypothesize that certain human gestures, which can be automatically identified through techniques of computer vision, can convey significant information that are correlated to audience engagement. We present a joint-angle descriptor derived from an automatic upper body pose estimation framework to train an SVM which identifies point and spread poses in extracted video frames of an instructor giving a lecture. Ground-truth is collected in the form of 2500 manually annotated frames covering 20 minutes of a video lecture. Cross validation on the ground-truth data showed classifier F-scores of 0.54 and 0.39 for point and spread poses, respectively. We also derive an attribute for gestures which measures the angular variance of the arm movements from this system (analogous to arm waving). We present a method for tracking hands which succeeds even when left and right hands are clasping and occluding each other. We evaluate on a ground-truth dataset of 698 images with 1301 annotated left and right hands, mostly clasped. Our method performs better than baseline on recall (0.66 vs. 0.53) without sacrificing precision (0.65 for both) toward the goal of recognizing clasped hands. For tracking, it results in an improvement over a baseline method with an F-score of 0.59 vs. 0.48. From this, we are able to derive hand motion-based gesture attributes such as velocity, direction change and extremal pose. In ground-truth studies, we manually annotate and analyze the gestures of two instructors, each in a 75-minute computer science lecture using a 14-bit pose vector. We observe "pedagogical" gestures of punctuation and encouragement in addition to traditional classes of gestures such as deictic and metaphoric. We also introduce a tool to facilitate the manual annotations of gestures in video and present results on their f…
abstract: In this thesis, we argue that in the domains of educational lectures and political debates, speaker gestures can be a source of semantic cues for video browsing. We hypothesize that certain human gestures, which can be automatically identified through techniques of computer vision, can convey significant information that are correlated to audience engagement. We present a joint-angle descriptor derived from an automatic upper body pose estimation framework to train an SVM which identifies point and spread poses in extracted video frames of an instructor giving a lecture. Ground-truth is collected in the form of 2500 manually annotated frames covering 20 minutes of a video lecture. Cross validation on the ground-truth data showed classifier F-scores of 0.54 and 0.39 for point and spread poses, respectively. We also derive an attribute for gestures which measures the angular variance of the arm movements from this system (analogous to arm waving). We present a method for tracking hands which succeeds even when left and right hands are clasping and occluding each other. We evaluate on a ground-truth dataset of 698 images with 1301 annotated left and right hands, mostly clasped. Our method performs better than baseline on recall (0.66 vs. 0.53) without sacrificing precision (0.65 for both) toward the goal of recognizing clasped hands. For tracking, it results in an improvement over a baseline method with an F-score of 0.59 vs. 0.48. From this, we are able to derive hand motion-based gesture attributes such as velocity, direction change and extremal pose. In ground-truth studies, we manually annotate and analyze the gestures of two instructors, each in a 75-minute computer science lecture using a 14-bit pose vector. We observe "pedagogical" gestures of punctuation and encouragement in addition to traditional classes of gestures such as deictic and metaphoric. We also introduce a tool to facilitate the manual annotations of gestures in video and present results on their f…
author: Zhang, John
language: English
keywords: Computer science, Communication
date published: 2013
date modified: 2026-07-24T01:44:36Z
provider: Columbia University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Columbia University

PropertyValue
property ID: DOI
value: 10.7916/D8CF9XGS