清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

DBN versus HMM for Gesture Recognition in Human-Robot Interaction

手势 计算机科学 隐马尔可夫模型 手势识别 接口(物质) 人机交互 机器人 人工智能 语音识别 人机交互 背景(考古学) 用户界面 计算机视觉 古生物学 气泡 最大气泡压力法 并行计算 生物 操作系统
作者
Brice Burger,Guillaume Infantes,Isabelle Ferrané,Frédéric Lerasle
摘要

Abstract: We designed an easy-to-use user interface based on speech and gesture modalities for controling an interactive robot. This paper, after a brief description of this interface and the platform on which it is implemented, describes an embedded gesture recognition system which is part of this multimodal interface. We describe two methods, namely Hidden Markov Models and Dynamic Bayesian Networks, and discuss their relative performance for this task in our Human-Robot interaction context. The implementation of our DBN-based recognition is outlined and some quantitative results are shown. I. INTRODUCTIONSince assistant robots are designed to directly interact with people, finding natural and easy-to-use user interfaces is of fundamental importance [1]. Nevertheless, few robotic systems are currently equipped with a completely on-board multimodal user interface enabling robot control through communication channels like speech, gesture or both. The most advanced one is [2] in which a constraint based multimodal system for speech and 3D pointing gestures has been developed, but gesture recognition is limited to mono-manual pointing gestures. In other works, like [3] and [4], gesture recognition is often extracted from monocular images, loosing the depth information and thus losing the capability of dealing with a pointing gesture other than directional. With the intention of providing our interactive robot called Jido with such an interface, we developed both speech and gesture recognition systems as well as a module for fusing these two information results. This merging step enables to:− complete an underspecified sentence, an abbreviation or an omission, which is usual in human communication particularly if a gesture can be done or even used instead− strengthen each modality by improving the classification rates of multimodal commands thanks to a probabilistic merge of gesture and speech recognition results.In this framework, this paper focuses on our one- and two-handed gesture recognition system given the video stream delivered by the on-board stereo head, with the physical constraints imposed by autonomous robotic systems in background: mobility of the platform, limited and shared computational power, limited memory capacities, etc.First section describes as a background our platform and the interface we developed on it, leading to an explanation of our needs in gesture recognition. Next, we discuss the relative performance of Hidden Markov Models (HMM) and Dynamic Bayesian Networks (DBN) for such a task, given the output of our 3D visual tracker devoted to the upper human body extremities [5]. Then, the implementation of our DBN-based recognition is outlined. We describe more precisely the data clustering process which is carried out thanks to a Kohonen network, the model training made by means of an Expectation-Maximization based algorithm and the recognition performed using particle filtering [6]. Finally, some qualitative and quantitative results from a symbolic and deictic gesture database are presented. The DBN representation, which is commonly used for human activity recognition, is shown to outperform the HMM representation especially in terms of CPU time consuming and gesture segmentation.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI2S应助科研通管家采纳,获得10
6秒前
笨笨完成签到 ,获得积分10
8秒前
芒芒发paper完成签到 ,获得积分10
24秒前
顺心蜜粉发布了新的文献求助30
28秒前
顺心蜜粉完成签到,获得积分10
39秒前
40秒前
CC发布了新的文献求助10
45秒前
淞淞于我完成签到 ,获得积分10
56秒前
Jenny发布了新的文献求助50
1分钟前
CC完成签到,获得积分10
1分钟前
天天开心完成签到 ,获得积分10
1分钟前
Qian完成签到 ,获得积分10
1分钟前
2分钟前
玄之又玄完成签到,获得积分10
2分钟前
糯米团的完成签到 ,获得积分10
2分钟前
爆米花应助ceeray23采纳,获得20
2分钟前
爆米花应助彦嘉采纳,获得10
3分钟前
3分钟前
ceeray23发布了新的文献求助20
3分钟前
zpc猪猪完成签到,获得积分10
3分钟前
Jenny发布了新的文献求助50
3分钟前
香蕉觅云应助ceeray23采纳,获得20
3分钟前
hoshi完成签到 ,获得积分10
3分钟前
woxinyouyou完成签到,获得积分0
3分钟前
思源应助方俊驰采纳,获得10
4分钟前
yuyuyu完成签到 ,获得积分10
4分钟前
4分钟前
方俊驰发布了新的文献求助10
4分钟前
方俊驰完成签到,获得积分10
4分钟前
4分钟前
ceeray23发布了新的文献求助20
4分钟前
研友_8y2G0L完成签到,获得积分10
4分钟前
liwang9301完成签到,获得积分10
4分钟前
5分钟前
5分钟前
lyj完成签到 ,获得积分10
5分钟前
Xuancheng_SINH完成签到,获得积分10
5分钟前
郭俊秀完成签到 ,获得积分10
5分钟前
5分钟前
ChatGPT发布了新的文献求助10
5分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990550
求助须知:如何正确求助?哪些是违规求助? 3532220
关于积分的说明 11256532
捐赠科研通 3271057
什么是DOI,文献DOI怎么找? 1805207
邀请新用户注册赠送积分活动 882302
科研通“疑难数据库(出版商)”最低求助积分说明 809234