Automatic reconstruction of 3D human arm motion from a monocular image sequence

Filova, Valentina; Solina, Franc; Lenarčič, Jadran

doi:10.1007/s001380050074

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…An imagebased method calculates image similarity by differentiating time-lagged frames and then estimates the period [4]. To reconstruct 3D human arm motion, Filova et al introduced a model-based method using a 2D shape model and the constraints of a 3D kinematic model [7].…”

Section: Beat Gesture Recognitionmentioning

confidence: 99%

Beat gesture recognition and finger motion control of a piano playing robot for affective interaction of the elderly

Park¹,

Jeong²,

Pelczar

et al. 2008

Intel Serv Robotics

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This paper introduces a piano playing robot in views of smart house and assistive robot technology to care the affective states of the elderly. We address the current issues in this research area and propose a piano playing robot as a solution. For affective interaction based on music, we first present a beat gesture recognition method to synchronize the tempo of a robot playing a piano with the desired tempo of the user. To estimate the period of an unstructured beat gesture expressed by any part of a body or an object, we apply an optical flow method, and use the trajectories of the center of gravity and normalized central moments of moving objects in images. In addition, we also apply a motion control method by which robotic fingers are trained to follow a set of trajectories. Since the ability to track the trajectories influences the sound a piano generates, we adopt an iterative learning control method to reduce the tracking error.

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Section: Beat Gesture Recognitionmentioning

confidence: 99%

Beat gesture recognition and finger motion control of a piano playing robot for affective interaction of the elderly

Park¹,

Jeong²,

Pelczar

et al. 2008

Intel Serv Robotics

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show abstract

“…8. Of course, the values of rotational parameters could also be constrained, as, for example, would be the case of a human arm [20], which can only move in certain ways, but this is beyond the scope of our work.…”

Section: Object Modelmentioning

confidence: 99%

Part-level object recognition using superquadrics

Krivic

Solina

2004

Computer Vision and Image Understanding

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“…These systems axe very useful in virtual reality applications. Solina et al [4] propose a model-based system that at first tracks the 2D silhouette of the human arm by means of iterative secondorder curves fitting, and then reconstructs the 3D pose using the knowledge related to the kinematic constraints of the arm itself. Pentlazad [1] uses 3D blobs features to build a model of the human body: 3D geometry is inferred in real-time using an uncalibrated stereo system.…”

Section: Introduction and Related Workmentioning

confidence: 99%

An architecture for automatic gesture analysis

Ardizzone

Chella

Pirrone

2000

Proceedings of the Working Conference on Advanced Visual Interfaces

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The field of human-computer interaction has been widely investigated in the last years, resulting in a variety of systems used in different application fields like virtual reality simulation environments, software user interfaces, and digital library systems. A very crucial part of all these systems is the input module which is devoted to recognize the human operator in terms of tracking and/or recognition of human face, arms position, hand gestures, and so on. In this work a software architecture is presented, for the automatic recognition of human arms poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turnleft", "turn-right', "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues vector of the pixels co-variance matrix. This kind of information is strictly related to the shape of the contour of the operator figure, and can be usefully employed in order to assess the arms' position. Finally, a support vector machine (SVM) is trained in order to classify different poses, using the eigenvalues array. A detailed description of the system is presented along with some remarks on the statistical analysis we used, and on SVM. The experimental results, and an outline of the usability of the system as a generic shape classification tool axe also reported.Permission to make digital or hand copie of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. AVI 2000, Palermo, Italy.

show abstract

Automatic reconstruction of 3D human arm motion from a monocular image sequence

Cited by 6 publications

References 20 publications

Beat gesture recognition and finger motion control of a piano playing robot for affective interaction of the elderly

Beat gesture recognition and finger motion control of a piano playing robot for affective interaction of the elderly

Part-level object recognition using superquadrics

An architecture for automatic gesture analysis

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