Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction

Fatai Idowu Sadiq; Ali Selamat; Roliana Ibrahim; Ondrej Krejcar

doi:10.3390/e21050487

Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction

Entropy (Basel). 2019 May 13;21(5):487. doi: 10.3390/e21050487.

Authors

Fatai Idowu Sadiq^{1

2}, Ali Selamat^{1

3

4}, Roliana Ibrahim¹, Ondrej Krejcar³

Affiliations

¹ Faculty of Engineering, School of Computing, UTM & Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia.
² Faculty of Physical Sciences, Ambrose Alli University, P.M.B 14, 310101 Ekpoma, Edo State, Nigeria.
³ Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 500 03 Hradec Kralove, Czech Republic.
⁴ Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia.

Abstract

Sensor technology provides the real-time monitoring of data in several scenarios that contribute to the improved security of life and property. Crowd condition monitoring is an area that has benefited from this. The basic context-aware framework (BCF) uses activity recognition based on emerging intelligent technology and is among the best that has been proposed for this purpose. However, accuracy is low, and the false negative rate (FNR) remains high. Thus, the need for an enhanced framework that offers reduced FNR and higher accuracy becomes necessary. This article reports our work on the development of an enhanced context-aware framework (EHCAF) using smartphone participatory sensing for crowd monitoring, dimensionality reduction of statistical-based time-frequency domain (SBTFD) features, and enhanced individual behavior estimation (IBE_enhcaf). The experimental results achieved 99.1% accuracy and an FNR of 2.8%, showing a clear improvement over the 92.0% accuracy, and an FNR of 31.3% of the BCF.

Keywords: accuracy; context-aware framework; false negative rate; individual behavior estimation; statistical-based time-frequency domain and crowd condition.