Clifton Phua, Kelvin Sim, Jit Biswas


Activity recognition of a single person in a smart space, using simple sensors, has been an ongoing research problem for the past decade, as simple sensors are cheap and non-intrusive. Recently, there is rising interest on multiple people activity recognition (MPAT) in a smart space with simple sensors, because it is common to have more than one person in real-world environments. We present the existing approaches of MPAT, such as Hidden Markov Models, and the available multiple people activities datasets. In our experiments, we show that surprisingly, without the use of existing approaches of MPAT, even standard classification techniques can yield high accuracy. We conclude that this is due to a set of assumptions that hold for the datasets that we used and this may be unrealistic in real life situations. Finally, we discuss the open challenges of MPAT, when these set of assumptions do not hold.


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Paper Citation

in Harvard Style

Phua C., Sim K. and Biswas J. (2011). MULTIPLE PEOPLE ACTIVITY RECOGNITION USING SIMPLE SENSORS . In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-8425-48-5, pages 224-231. DOI: 10.5220/0003399902240231

in Bibtex Style

author={Clifton Phua and Kelvin Sim and Jit Biswas},
booktitle={Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,},

in EndNote Style

JO - Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,
SN - 978-989-8425-48-5
AU - Phua C.
AU - Sim K.
AU - Biswas J.
PY - 2011
SP - 224
EP - 231
DO - 10.5220/0003399902240231