Beamsteering for Training-free Counting of Multiple Humans Performing Distinct Activities
| Autor: | Sameera Palipana, Nicolas Malm, Stephan Sigg |
|---|---|
| Přispěvatelé: | Department of Communications and Networking, Aalto-yliopisto, Aalto University |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Beamforming
Beamsteering business.industry Computer science Multi-subject recognition 020302 automobile design & engineering 020206 networking & telecommunications Context (language use) Pattern recognition 02 engineering and technology Intrusion detection system Radio sensing Training-free crowd counting Blind signal separation Spatial multiplexing 0203 mechanical engineering 0202 electrical engineering electronic engineering information engineering Artificial intelligence Antenna (radio) business Cluster analysis Gesture |
| Zdroj: | PerCom |
| Popis: | Recognition of the context of humans plays an important role in pervasive applications such as intrusion detection, human density estimation for heating, ventilation and air-conditioning in smart buildings, as well as safety guarantee for workers during human-robot interaction. Radio vision is able to provide these sensing capabilities with low privacy intrusion. A common challenge though, for current radio sensing solutions is to distinguish simultaneous movement from multiple subjects. We present an approach that exploits antenna installations, for instance, found in upcoming 5G technology, to detect and extract activities from spatially scattered human targets in an ad-hoc manner in arbitrary environments and without prior training of the multi-subject detection. We perform receiver-side beamforming and beam-sweeping over different azimuth angles to detect human presence in those regions separately. We characterize the resultant fluctuations in the spatial streams due to human influence using a case study and make the traces publicly available. We demonstrate the potential of this approach through two applications: 1) By feeding the similarities of the resulting spatial streams into a clustering algorithm, we count the humans in a given area without prior training. (up to 6 people in a 22.4 m2 area with an accuracy that significantly exceeds the related work). 2) We demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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