Improved estimation of hand postures using depth images

Hamester, Dennis; Jirak, Doreen; Wermter, Stefan

doi:10.1109/icar.2013.6766485

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al (2011) track the hands' location and segment them using a region-growing algorithm. Hamester et al (2013) detect hands in depth images based on Fourier descriptors of contours classified using a SVM. Joo et al (2014) use boosting of depth-difference features for detecting hands in depth images.…”

Section: Computing Features From Sensorsmentioning

confidence: 99%

Multi-users online recognition of technical gestures for natural human–robot collaboration in manufacturing

2018

View full text Add to dashboard Cite

Human-Robot Collaboration in industrial context requires a smooth, natural and efficient coordination between robot and human operators. The approach we propose to achieve this goal is to use online recognition of technical gestures. In this paper, we present together, and analyze, parameterize and evaluate much more thoroughly, three findings previously unveiled separately by us in several conference presentations: 1/ we show on a real prototype that multi-users continuous real-time recognition of technical gestures on an assembly-line is feasible (≈ 90% recall and precision in our case-study), using only non-intrusive sensors (depth-camera with a top-view, plus inertial sensors placed on tools); 2/ we formulate an end-to-end methodology for designing and developing such a system; 3/ we propose a method for adapting to new users our gesture recognition. Furthermore we present here two new findings: 1/ by comparing recognition performances using several sets of features, we highlight the importance of choosing features that focus on the effective part of gestures, i.e. usually hands movements; 2/ we obtain new results suggesting that enriching a multiusers training set can lead to higher precision than using a separate training dataset for each operator.

show abstract

Section: Computing Features From Sensorsmentioning

confidence: 99%

Multi-users online recognition of technical gestures for natural human–robot collaboration in manufacturing

2018

View full text Add to dashboard Cite

show abstract

Real-Time Hand Detection by Depth Images: A Survey

et al. 2016

View full text Add to dashboard Cite

Human hand detection can enable human to communicate with a machine and interact without any external device. Human hands play an important role in different applications such as medical image processing, sign language translator, gesture recognition and augmented reality. A human hand has different length and breadth for male and female. So, it is a complex articulated object consisting many connected parts and joints. Traditional methods for hand detection and tracking used color and shape information from RGB camera. Using a depth camera for hand detection and tracking is a challenging and interesting domain in computer vision. Some research has shown that using depth data for hand detection can improve human computer interaction. Recently, researchers used depth data in different hand detection and tracking methods in real time application. This paper explains different types of methods which are used for human hand detection. Various techniques and methods are explored and analyzed in this survey to determine the shortfalls and future directions in the field of hand detection from depth data.

show abstract

Improved estimation of hand postures using depth images

Cited by 2 publications

References 15 publications

Multi-users online recognition of technical gestures for natural human–robot collaboration in manufacturing

Multi-users online recognition of technical gestures for natural human–robot collaboration in manufacturing

Real-Time Hand Detection by Depth Images: A Survey

Contact Info

Product

Resources

About