Person Tracking Association Using Multi-modal Systems
| Autor: | Thomas Theodoridis, Vassilios Solachidis, Alberto Belmonte-Hernandez, Nicholas Vretos, Federico Alvarez, Giuseppe Conti, Petros Daras, Gustavo Hernandez-Penaloza |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Matching (statistics) Telecomunicaciones business.industry Computer science Feature extraction Robótica e Informática Industrial ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Wearable computer 02 engineering and technology Visualization 020901 industrial engineering & automation Modal Inertial measurement unit Synchronization (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Wearable technology |
| Zdroj: | IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2017) | IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2017) | 29/08/2017-01/09/2017 | Lecce, Italy Archivo Digital UPM Universidad Politécnica de Madrid AVSS 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
| Popis: | In this paper, a novel multi-modal method for person identification in indoor environments is presented. This approach relies on matching the skeletons detected by a Kinect v2 device with wearable devices equipped with inertial sensors. Movement features such as yaw and pitch changes are employed to associate a particular Kinect skeleton to a person using the wearable. The entire process of sensor calibration, feature extraction, synchronization and matching is detailed in this work. Six detection scenarios were defined to assess the proposed method. Experimental results have shown a high accuracy in the association process. |
| Databáze: | OpenAIRE |
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