A camera-based interface for interaction with mobile handheld computers

Hachet, Martin; Pouderoux, Joachim; Guitton, Pascal

doi:10.1145/1053427.1053438

Cited by 52 publications

(34 citation statements)

References 20 publications

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“…Möhring et al [4] presented a tracking system for augmented reality on a mobile phone to estimate 3D camera pose using special colour coded markers. Other marker-based methods use a printed or hand-drawn circle [5], a hand-held target [6], and a set of squares [7] to facilitate the control task. One new solution was presented by Pears [8].…”

Section: Related Workmentioning

confidence: 99%

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Camera-Based Motion Recognition for Mobile Interaction

Hannuksela

Barnard

Sangi

et al. 2011

ISRN Signal Processing

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Multiple built-in cameras and the small size of mobile phones are underexploited assets for creating novel applications that are ideal for pocket size devices, but may not make much sense with laptops. In this paper we present two vision-based methods for the control of mobile user interfaces based on motion tracking and recognition. In the first case the motion is extracted by estimating the movement of the device held in the user's hand. In the second it is produced from tracking the motion of the user's finger in front of the device. In both alternatives sequences of motion are classified using Hidden Markov Models. The results of the classification are filtered using a likelihood ratio and the velocity entropy to reject possibly incorrect sequences. Our hypothesis here is that incorrect measurements are characterised by a higher entropy value for their velocity histogram denoting more random movements by the user. We also show that using the same filtering criteria we can control unsupervised Maximum A Posteriori adaptation. Experiments conducted on a recognition task involving simple control gestures for mobile phones clearly demonstrate the potential usage of our approaches and may provide for ingredients for new user interface designs.

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Section: Related Workmentioning

confidence: 99%

“…We assume that the majority of features are extracted from the background. Therefore, the initial probabilities π 1 and π 2 (see (6)) for the background and the finger motion were set to 0.7 and 0.3, respectively. The learning rate a in (11) was set to 0.95 that guarantees a decent change in the proportion of mixture components.…”

Section: Objectmentioning

confidence: 99%

Camera-Based Motion Recognition for Mobile Interaction

Hannuksela

Barnard

Sangi

et al. 2011

ISRN Signal Processing

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“…There were several previous researchers who tried to use the camera as an interface for user interactions, especially for 3D interactions [8,9]. However, robust user's motion tracking is not an easy problem, particularly in unconstrained environments and with the limited computational power of mobile phones [10].…”

Section: Related Workmentioning

confidence: 99%

“…Hachet et al used a camera-equipped hand-held device as a prop for interaction in a virtual environment. In their system, the hand-held camera recognized the movement and poses of a special marker held in the other hand [9,13]. The user could interact in the virtual environment (seen through a separate large display in front of him) by detecting the motion of the marker by the camera.…”

Section: Related Workmentioning

confidence: 99%

Interaction with large ubiquitous displays using camera-equipped mobile phones

Jeon

Hwang

Kim

et al. 2009

Pers Ubiquit Comput

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In the ubiquitous computing environment, people will interact with everyday objects (or computers embedded in them) in ways different from the usual and familiar desktop user interface. One such typical situation is interacting with applications through large displays such as televisions, mirror displays, and public kiosks. With these applications, the use of the usual keyboard and mouse input is not usually viable (for practical reasons). In this setting, the mobile phone has emerged as an excellent device for novel interaction. This article introduces user interaction techniques using a camera-equipped hand-held device such as a mobile phone or a PDA for large shared displays. In particular, we consider two specific but typical situations (1) sharing the display from a distance and (2) interacting with a touch screen display at a close distance. Using two basic computer vision techniques, motion flow and marker recognition, we show how a camera-equipped hand-held device can effectively be used to replace a mouse and share, select, and manipulate 2D and 3D objects, and navigate within the environment presented through the large display.

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“…But it can also be used the other way around. A nice example for bringing the mobility and spatial awareness of a device to the virtual world is presented by Hachet et al (Hachet et al, 2005). They use a mobile device capable of displaying 3D graphic to interact with the objects in the virtual world.…”

Section: Peephole Displaysmentioning

confidence: 99%