Orientation estimation in modern wearables with visual feature tracking

Kundra, Laszlo; Ekler, Péter; Charaf, Hassan

doi:10.1007/s12193-015-0180-9

Cited by 2 publications

(9 citation statements)

References 23 publications

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“…Erdős [135], [136] determined very precisely G 2 (n). In [7] G k (n) is determined asymptotically precisely for every k, moreover both lower and upper bounds are given for the "error term". For instance, for k=3 we show that π(n)+π(n/2)+cn 2/3 (log n) 4/3 ≤ G 3 (n)≤π(n)+π(n/2)+cn 2/3 (log n)/(loglog n), and for k=4 π(n)+n 3/5 / (log n) 6/5 ≤G 4 (n)≤π(n)+cn 2/3 (where π(x) denotes the number of primes up to x).…”

Section: Further Combinatorial Resultsmentioning

confidence: 99%

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BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

Vajk¹,

Harsányi

Poppe

et al. 2016

Period. Polytech. Elec. Eng. Comp. Sci.

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PrefaceThroughout László Vajta, Dean of BME VIK János Levendovszky, Deputy Dean of BME VIK Department of Automation and Applied InformaticsDepartment of Automation and Applied Informatics (AUT) is one of the largest and dynamically developing departments at BME with diverse scope competences such as control theory, embedded systems, software modelling, applied software development, Internet of Things, power electronics, mechatronics, and many others. The department's main activities are education, research and development. Training versatile electrical and software engineers with solid practical knowledge is our top priority. Our profile also includes developing high quality software and hardware solutions for industry partners. All of our activities are backed by strong research background.In 2015, the department has organized the 21 st European Wireless (EW) Conference. The EW 2015 conference hosted more than 140 participants from 30 different countries. The 2015 edition of EW was aimed at addressing a key theme on "5G and beyond."The next sections summarize the key research results of the department in year 2015. IoT and Smart CityThe Internet of Things (IoT) is transforming the surrounding physical objects into an ecosystem of information that enriches our everyday life. The IoT represents the convergence of advances in miniaturization, wireless connectivity, and increased data storage, and is driven by various sensors. Application and service development methods and frameworks are required to support the realization of solutions covering data collection, transmission, data processing, analysis, reporting, and advanced querying. Our SensorHUB framework URBMOBI is a mobile environmental sensor that (i) provides temporally and spatially distributed environmental data, (ii) fulfills the need for monitoring at various places without the costs for a large number of fixed measurement stations, (iii) integrates small and precise sensors in a system that can be operated on buses, trams, or other vehicles, (iv) concentrates on urban heat and thermal comfort, and (v) aims at providing climate services and integration with real-time climate models.utilizes the state-of-the-art open source technologies and provides a unified tool chain for IoT related application and ser-URBMOBI project has been worked out between 2013 and 2015 by the following consortium: RWTH Aachen University (Germany), Netherlands Organization for Applied Scientific Research TNO (Netherlands), ARIA Technologies (France), Budapest University of Technology and Economics (Hungary), and Meteorological and Environmental Earth Observation (Italy).The SOLSUN (Sustainable Outdoor Lighting & Sensory Urban Networks) project is about to demonstrate how intelligent city infrastructure can be created in a cost-effective and sustainable way by reusing existing street lighting as the communications backbone. We apply different technologies and methods to reduce energy consumption at the same time as turning streetlights into nodes on a scalable network that is al...

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Section: Further Combinatorial Resultsmentioning

confidence: 99%

“…In addition to that, the multimedia hardware on phones like the camera allows implementing special solutions. In [7], we have investigated the possibility of augmented sensing with the camera and we have shown a special method for orientation estimation. …”

mentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

Vajk¹,

Harsányi

Poppe

et al. 2016

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

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“…The results of 100 experiments were selected for the identification of each semantic gesture, and the recognition rate is shown in Table 1. Table 1 shows the recognition rate of gesture recognition in the model proposed in this paper and in the literature [13][14][15]. The literatures [13][14][15] are currently more advanced and mature technology.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The literatures [13][14][15] are currently more advanced and mature technology. It can be seen from Table 3 that the average recognition rates of literature [13], literature [14], and literature [15] are 89%, 88.42%, and 86.50%, respectively, and the average recognition rate of the model is 96.17% (Fig. 7).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…In the scene, the method based on the marker hand brings convenience to the gesture recognition process, but it creates difficulties for the operator, and the gesture recognition based on the natural human hand is favored by the researchers because of its naturalness and intuitiveness. The research on gesture recognition based on monocular vision is a very meaningful topic, which helps to deepen the human-centered human-computer interaction [15]. However, gesture recognition is a challenging research task due to the variability, randomness, and spatial and temporal uncertainty of the gesture itself.…”

Section: Introductionmentioning

confidence: 99%

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Gesture recognition algorithm based on image information fusion in virtual reality

Jun

2019

Pers Ubiquit Comput

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Combining image information fusion theory with machine learning for biometric recognition is an important field in computer vision research in recent years. Based on this, a gesture recognition algorithm based on image information fusion in virtual reality is proposed. Firstly, it introduces the basic concepts and principles of virtual reality and information fusion technology, analyzes the characteristics and basic components of virtual environment system, points out the relationship between human and virtual environment and the impact of virtual environment on people, and gives a virtual reality. Then, the multi-sensor information fusion model of the virtual environment for gesture recognition is proposed. The membership degree and template matching algorithm are further selected for data correlation and gesture recognition in the fusion model. Finally, the design comparison experiment verifies the proposed method. The results show that the proposed multi-sensor information fusion model in the interactive virtual environment achieves the highest recognition success rate of 96.17% and is better than several comparison machine learning methods in recognition time.

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Orientation estimation in modern wearables with visual feature tracking

Cited by 2 publications

References 23 publications

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

Gesture recognition algorithm based on image information fusion in virtual reality

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