Fast camera motion estimation for hand-held devices and applications

Liu, Xu; Doermann, David; Li, Huiping

doi:10.1145/1149488.1149505

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…A number of authors have examined the possibility of using phone motion to draw alphanumeric characters. Liu et al [14] show examples of Latin and Chinese characters drawn using the ego-motion of a mobile device, although these characters are not recognised or used for control. Kratz and Ballagas [15] propose using a simple set of motions to interact with the external environment through the mobile device.…”

Section: Related Workmentioning

confidence: 99%

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%

Camera-Based Motion Recognition for Mobile Interaction

Hannuksela

Barnard

Sangi

et al. 2011

ISRN Signal Processing

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“…2. In previous work, we have used a series of phones with different platforms (Symbian and Windows Mobile) for a camera browsing project [21]. Today the mobile devices with imaging capabilities are evolving quickly.…”

Section: Architecturementioning

confidence: 99%

VCode—Pervasive Data Transfer Using Video Barcode

Liu¹,

Doermann²,

Li³

2008

IEEE Trans. Multimedia

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In this paper, we describe a novel data transfer scheme that uses the camera in a smart phone as an alternative data channel. The data is encoded as a sequence of 2-D barcode images, displayed on a flat panel display, acquired by the camera, and decoded in real time by the software embedded in device. The decoded data is written to a file. Compared with existing data channels, such as CDMA/GPRS, cables, Bluetooth, and Infrared, our method relies on visual communication and does not require special hardware or data plans. Users only need to point the camera at a monitor displaying the VCode to download. Technical challenges to overcome include correction of perspective distortion, compensation for contrast variation, and efficient implementation of small footprint software into a mobile device. We address these challenges and present our solution in detail. We have implemented a prototype which allows users to download various types of files successfully, including pictures, ring tones and Java games onto camera phones running Symbian and Windows Mobile platforms. We discuss the limitations of our solution and outline future work to overcome these limitations.

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“…However, most of these techniques are only implemented for simple motion pattern estimation, such as pan, tilt, zoom and rotation [5]. Moreover, the performance of these systems is not satisfying enough under severe conditions, such as writing speeds, variation in illumination conditions and so forth [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Digit-writing hand gesture recognition by hand-held camera motion analysis

Hao

Shibata

2009

2009 3rd International Conference on Signal Processing and Communication Systems

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A camera motion detection and analysis algorithm applicable to hand-held devices, such as mobile phones, has been developed and applied to digit-writing gesture recognition. The writing stroke is recorded from an image sequence taken by a moving camera. The automatic speed adaptation capability developed in the motion detection system has enabled very robust writing stroke detection. As a result, the temporal stroke distortion due to irregular writing speed has been eliminated. Since both the direction and magnitude of motion is detected at each instant, the writing stroke is correctly reconstructed by integrating the results. For this reason, feature vector for each digit character was constructed by connecting feature distribution in each direction. As a result, handwriting gesture recognition is achieved by simple template matching. The system performance has been evaluated by digit-writing gesture recognition with irregular writing speed, different users, or cursive writing. Preliminary experiments on hand-writing Chinese character recognition have also been attempted and the potentiality of the algorithm for more complicated gesture patterns has been tested.

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Fast camera motion estimation for hand-held devices and applications

Cited by 9 publications

References 15 publications

Camera-Based Motion Recognition for Mobile Interaction

Camera-Based Motion Recognition for Mobile Interaction

VCode—Pervasive Data Transfer Using Video Barcode

Digit-writing hand gesture recognition by hand-held camera motion analysis

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