دورية أكاديمية
Machine-learning-based system for multi-sensor 3D localisation of stationary objects
| العنوان: | Machine-learning-based system for multi-sensor 3D localisation of stationary objects |
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| المؤلفون: | Everton L. Berz, Deivid A. Tesch, Fabiano P. Hessel |
| المصدر: | IET Cyber-Physical Systems (2018) |
| بيانات النشر: | Wiley, 2018. |
| سنة النشر: | 2018 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | indoor navigation, object detection, radiofrequency identification, support vector machines, regression analysis, neural nets, computer vision, learning (artificial intelligence), image fusion, machine-learning-based system, multisensor 3D localisation, stationary objects, object localisation, people localisation, indoor environments, security issues, Indoor positioning systems, localisation performance, multisensor IPS, three-dimensional location, radio-frequency identification technology, support vector regression, artificial neural networks, k-means technique, computer vision subsystem, visual markers, RFID localisation, CV subsystems, region of interest, bi-dimensional scenarios, localisation error, ANN models, SVR models, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Localisation of objects and people in indoor environments has been widely studied due to security issues and because of the benefits that a localisation system can provide. Indoor positioning systems (IPSs) based on more than one technology can improve localisation performance by leveraging the advantages of distinct technologies. This study proposes a multi-sensor IPS able to estimate the three-dimensional (3D) location of stationary objects using off-the-shelf equipment. By using radio-frequency identification (RFID) technology, machine-learning models based on support vector regression (SVR) and artificial neural networks (ANNs) are proposed. A k-means technique is also applied to improve accuracy. A computer vision (CV) subsystem detects visual markers in the scenario to enhance RFID localisation. To combine the RFID and CV subsystems, a fusion method based on the region of interest is proposed. We have implemented the authors’ system and evaluated it using real experiments. On bi-dimensional scenarios, localisation error is between 9 and 29 cm in the range of 1 and 2.2 m. In a machine-learning approach comparison, ANN performed 31% better than SVR approach. Regarding 3D scenarios, localisation errors in dense environments are 80.7 and 73.7 cm for ANN and SVR models, respectively. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2398-3396 |
| Relation: | https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2017.0067; https://doaj.org/toc/2398-3396 |
| DOI: | 10.1049/iet-cps.2017.0067 |
| URL الوصول: | https://doaj.org/article/2d5247848e5446f38134226aec95e6b7 |
| رقم الأكسشن: | edsdoj.2d5247848e5446f38134226aec95e6b7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23983396 |
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| DOI: | 10.1049/iet-cps.2017.0067 |