Proceedings Article10.1109/ICHR.2008.4755973
Recognizing complex, parameterized gestures from monocular image sequences
T. Axenbeck,Maren Bennewitz,Sven Behnke,Wolfram Burgard +3 more
- 01 Dec 2008
- pp 687-692
TL;DR: A system that is able to spot and recognize complex, parameterized gestures from monocular image sequences by using few, expressive features extracted out of this compact representation as input to hidden Markov models (HMMs).
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Abstract: Robotic assistants designed to coexist and communicate with humans in the real world should be able to interact with them in an intuitive way. This requires that the robots are able to recognize typical gestures performed by humans such as head shaking/nodding, hand waving, or pointing. In this paper, we present a system that is able to spot and recognize complex, parameterized gestures from monocular image sequences. To represent people, we locate their faces and hands using trained classifiers and track them over time. We use few, expressive features extracted out of this compact representation as input to hidden Markov models (HMMs). First, we segment gestures into distinct phases and train HMMs for each phase separately. Then, we construct composed HMMs, which consist of the individual phase-HMMs. Once a specific phase is recognized, we estimate the parameter of the current gesture, e.g., the target of a pointing gesture. As we demonstrate in the experiments, our method is able to robustly locate and track hands, despite of the fact that they can take a large number of substantially different shapes. Based on this, our system is able to reliably spot and recognize a variety of complex, parameterized gestures.
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Citations
Real-Time Recognition of Pointing Gestures for Robot to Robot Interaction
Polychronis Kondaxakis,Joni Pajarinen,Ville Kyrki +2 more
- 06 Nov 2014
TL;DR: The results indicate that real-time detection of pointing gesture can be performed with little information about the embodiment of the pointing agent and that an observing agent can use the gesture detection to perform actions on the pointed targets.
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•Dissertation
Gestion de la variabilité morphologique pour la reconnaissance de gestes naturels à partir de données 3D
Anthony Sorel
- 06 Dec 2012
TL;DR: In this paper, a description of a mouvement permettant de repondre a cette problematique and evaluons sa capacite a reconnaitre lesmouvements naturels d'un utilisateur inconnu.
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Robot–Robot Gesturing for Anchoring Representations
TL;DR: A planning framework that minimizes the required effort for anchoring representations across robots and considers both implicit sources of failure, such as ambiguous pointing, as well as costs required by actions is described.
Intuitive multimodal interaction for service robots
Matthias Nieuwenhuisen,Jörg Stückler,Sven Behnke +2 more
- 02 Mar 2010
TL;DR: This report presents the communication skills of the anthropomorphic service and communication robots Dynamaid and Robotinho, equipped with an intuitive multimodal communication system, including speech synthesis and recognition, gestures and mimic.
The Effect of Multiple Training Sequences on HMM Classification of Motion Capture Gesture Data
Michał Romaszewski,Przemysław Głomb +1 more
- 01 Jan 2011
TL;DR: This work uses HMM as a model of a single gesture, and assess its recognition performance for multiple data sequences consisting of repetitions of selected gestures, performed by different persons with varying speed of movement.
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