TY  - JOUR
AU  - Ikechukwu Ofodile
AU  - Ahmed Helmi
AU  - Albert Clapes
AU  - Egils Avots
AU  - Kerttu Maria Peensoo
AU  - Sandhra Mirella Valdma
AU  - Andreas Valdmann
AU  - Heli Valtna Lukner
AU  - Sergey Omelkov
AU  - Sergio Escalera
AU  - Cagri Ozcinar
AU  - Gholamreza Anbarjafari
PY  - 2019//
TI  - Action recognition using single-pixel time-of-flight detection
T2  - ENTROPY
JO  - Entropy
SP  - 414
VL  - 21
IS  - 4
KW  - single pixel single photon image acquisition
KW  - time-of-flight
KW  - action recognition
N2  - Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrentneural network.
UR  - https://www.mdpi.com/1099-4300/21/4/414
UR  - http://dx.doi.org/10.3390/e21040414
N1  - HuPBA; no proj
ID  - Ikechukwu Ofodile2019
ER  -