Comparing Free Hand Menu Techniques for Distant Displays Using Linear, Marking and Finger-Count Menus

Bailly, Gilles; Walter, Robert; Müller, Jörg; Ning, Tongyan; Lecolinet, Éric

doi:10.1007/978-3-642-23771-3_19

Cited by 45 publications

(40 citation statements)

References 21 publications

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“…Bailly et al investigate and compare different menu techniques in [8]. Wilson and Benko developed a system with several projectors and depth cameras named LightSpace [9].…”

Section: Related Workmentioning

confidence: 99%

Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors

Kopinski

Gepperth

Geisler

et al. 2014

Artificial Neural Networks and Machine Learning – ICANN 2014

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Abstract. We present a study on 3D based hand pose recognition using a new generation of low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. We investigate the performance of different 3D descriptors, as well as the fusion of two ToF sensor streams. By basing a data fusion strategy on the fact that multilayer perceptrons can produce normalized confidences individually for each class, and similarly by designing information-theoretic online measures for assessing confidences of decisions, we show that appropriately chosen fusion strategies can improve overall performance to a very satisfactory level. Real-time capability is retained as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.

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“…Bailly et al investigate and compare different menu techniques in [8]. Wilson and Benko developed a system with several projectors and depth cameras named LightSpace [9].…”

Section: Related Workmentioning

confidence: 99%

Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors

Kopinski

Gepperth

Geisler

et al. 2014

Artificial Neural Networks and Machine Learning – ICANN 2014

View full text Add to dashboard Cite

show abstract

“…Bailly et al investigate and compare different menu techniques in [13]. Wilson and Benko developed a system with several projectors and depth cameras named LightSpace [14].…”

Section: Related Workmentioning

confidence: 99%

A real-time applicable 3D gesture recognition system for automobile HMI

Kopinski

Geisler

Caron

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-We present a system for 3D hand gesture recognition based on low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. Our system fuses data coming from two ToF sensors which is used to build up a large database and subsequently train a multilayer perceptron (MLP). We demonstrate that we are able to reliably classify a set of ten hand gestures in real-time and describe the setup of the system, the utilised methods as well as possible application scenarios. I. INTRODUCTIONAs "intelligent" devices enter more and more areas of everyday life, the issue of man-machine interaction becomes ever more important. As interaction should be easy and natural for the user and also not require a high cognitive load, non-verbal means of interaction such as hand gestures will play a decisive role in this field of research. With the advent of low-cost Kinect-type 3D sensors, and more recently of low-cost ToF sensors (400-500e) that can be applied in outdoor scenarios, the use of point clouds seems a very logical choice. This presents challenges to machine learning approaches as the data dimensionality and sensor noise are high, as well as the number of interesting gesture categories. In this article we build upon earlier results [1] and demonstrate how a system can be developed and integrated into a car in order to be able to classify a gesture alphabet of ten hand poses. Our approach is purely data-driven, i.e. by extending and applying a Pointcloud descriptor to our needs we are able to set up a real-time applicable system which is robust versus daylight interferences, invariant to rotation and translation problems and moreover works without the need to formalise a possibly complicated hand model. We will first discuss the related work relevant for our research (Sec. II) and then go on to describe the setup of our system within an automobile environment. Subsequently we describe the sensors and the used database in Sec. IV. In Sec. V we go on to give an account of the used different holistic point cloud descriptors and explain the meaning of the parameter variations we will test. Sec. VI summarises the implemented NN classes and the choice of parameters. The key questions we will investigate in Sec. VII concern the generalisation

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“…Conventional gesture controlled interfaces tend to misinterpret conversational gestures as input. A viable solution is the use of depth-activated marker menus [1]. The menu appearance is triggered by a fixed depth threshold with a Talwar-function hysteresis in order to compensate for non-linear arm movement (shown in Figure 3).…”

Section: Initial Findingsmentioning

confidence: 99%

Interface design for an inexpensive hands-free collaborative videoconferencing system

Lehment¹,

Erhardt²,

Rigoll³

2012

2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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In this paper an interaction framework for AR enhanced video conferencing is presented. The goal is to provide a cheap and portable system based on a combination of commodity Kinect cameras and regular computer screens. These conditions necessitate the use of contact free interaction methods. The interaction framework presented in this paper is specifically suited for remotely presenting, sharing and annotating visual data such as images, presentation slides and 3D objects. In the proposed system all data is represented by freely manipulable 3D objects which are augmented into the camera views. These representations are integrated into a differentiated ownership scheme, allowing for operations such as spatially managed data sharing. The suitability of different interaction paradigms with regards to this usage scenario is examined. Furthermore, occlusion and collision management between virtual objects and real obstacles is enabled by integrating basic models of the environment.

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Comparing Free Hand Menu Techniques for Distant Displays Using Linear, Marking and Finger-Count Menus

Cited by 45 publications

References 21 publications

Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors

Neural Network Based Data Fusion for Hand Pose Recognition with Multiple ToF Sensors

A real-time applicable 3D gesture recognition system for automobile HMI

Interface design for an inexpensive hands-free collaborative videoconferencing system

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