Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints

Oikonomidis, Iason; Kyriazis, Nicholas; Argyros, Antonis A.

doi:10.1109/iccv.2011.6126483

Cited by 252 publications

(267 citation statements)

References 18 publications

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“…These are reasonable constraints, since the industrial cell can be partly structured, and since image processing is not the main contribution of this work. More sophisticated approaches, such as [42,43], could be applied to relax these constraints.…”

Section: Image Processingmentioning

confidence: 99%

Collaborative manufacturing with physical human–robot interaction

Cherubini¹,

Passama²,

Crosnier³

et al. 2016

Robotics and Computer-Integrated Manufacturing

451

188

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Although the concept of industrial cobots dates back to 1999, most present day hybrid human-machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human-robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

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Section: Image Processingmentioning

confidence: 99%

Collaborative manufacturing with physical human–robot interaction

Cherubini¹,

Passama²,

Crosnier³

et al. 2016

Robotics and Computer-Integrated Manufacturing

451

188

View full text Add to dashboard Cite

show abstract

“…Oikonomidis (2012) tracks two interacting hands with Kinect input, introducing a penalty term measuring the inter-penetration of fingers to invalidate impossible articulated poses. Both Oikonomidis et al (2011b) and Kyriazis and Argyros (2013) track a hand and moving object simultaneously, and invalid configurations similarly penalized. In both cases the measure used is the minimum magnitude of 3D translation required to eliminate intersection of the two objects, a measure computed using the Open Dynamic Engine library (Smith 2006).…”

Section: Related Workmentioning

confidence: 99%

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

et al. 2017

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We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depthcolour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection.

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“…through the optimization of an objective function that quantifies the discrepancy between a hypothesis over the scene state and the actual observations. Whereas in [22] the scene amounted to a single hand, in this work, the scene comprises a hand and a rigid object, thus increasing the problem dimensionality to 32 DoFs, as in [21]. At each new tracking frame a new optimization is performed that is initialized in the vicinity of the solution for the previous frame.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…From these image sequences the parameters that regard the configuration of the subject's hand and the configuration of the object need to be extracted, so that they are provided for learning or inference. In order to extract such information we combine the methods in [21], [22] towards a system that can track an object and a hand, while in close interaction, in 3D, from RGB-D input. Tracking is performed as in [22], i.e.…”

Section: Data Acquisitionmentioning

confidence: 99%

Language for learning complex human-object interactions

Patel

Kyriazis

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-In this paper we use a Hierarchical Hidden Markov Model (HHMM) to represent and learn complex activities/task performed by humans/robots in everyday life. Action primitives are used as a grammar to represent complex human behaviour and learn the interactions and behaviour of human/robots with different objects. The main contribution is the use of a probabilistic model capable of representing behaviours at multiple levels of abstraction to support the proposed hypothesis. The hierarchical nature of the model allows decomposition of the complex task into simple action primitives. The framework is evaluated with data collected for tasks of everyday importance performed by a human user.

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Full DOF tracking of a hand interacting with an object by modeling occlusions and physical constraints

Cited by 252 publications

References 18 publications

Collaborative manufacturing with physical human–robot interaction

Collaborative manufacturing with physical human–robot interaction

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

Language for learning complex human-object interactions

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