Indoor positioning using smartphone camera

Werner, Martin; Kessel, Moritz; Marouane, Chadly

doi:10.1109/ipin.2011.6071954

Cited by 145 publications

(75 citation statements)

References 12 publications

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“…Using location information, a more specific and detailed location-based classifier can be applied. There are well-known ways to obtain location information indoors [18,19] and outdoors [20,21]. In this study, however, the location information is not collected from the system and derived from the type of activities.…”

Section: Classifiermentioning

confidence: 99%

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Kwon

Choi

2018

Wireless Communications and Mobile Computing

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Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

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Section: Classifiermentioning

confidence: 99%

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Kwon

Choi

2018

Wireless Communications and Mobile Computing

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“…Recent studies have been highly dependent on map [4]. Without the use of map, user can only see the environment provided from camera by using the screen.…”

Section: Augmented Realitymentioning

confidence: 99%

“…Concrete walls, objects and ceilings of the buildings can be suggested as main reasons for low/unavailable signal. The proximity between pathways inside the buildings may also cause complexity [4]. Since these complications occur, current location should be estimated by calculating through indoor navigation as in normal navigations.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Beacon-Enabled Mobile Indoor Navigation System Using Augmented Reality

Koyun

Çankaya

2018

Teh. vjesn.

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Abstract:In last decade, expansion of mobile phones increased people's usage of these devices. In parallel with the increasing usage rate, the number of developed applications increased for mobile phones. Thanks to mobile phones, many different fields have been affected. However, the biggest contributions of mobile phones are orientation and location finder services. Although people spend major time of their life in indoor areas, usage of navigation systems must be outdoors since these systems get coordinate information from satellites. In indoor areas, walls, and any object in a building cause wrong results or block navigation systems. In this study, orientation and location finder services for indoor navigation will be done by using accelerometer, compass and camera that have been already included in the phones and augmented reality technology for iOS based mobile phones and locations will be provided thanks to beacon Bluetooth devices.

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“…Image based navigation [9,10,11,13,14,15] satisfies the indoor navigation requirements. Because of the characteristics of the images, this technique supports users with rich visual information and enables a useful and novel user experience.…”

Section: Problem Statementmentioning

confidence: 99%

“…Werner et al [14] developed an image based indoor navigation application without using extra markers (sensors). The main idea and system structure are similar to the work described in [9].…”

Section: Comparison Of Our Work With the State Of The Artmentioning

confidence: 99%