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

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 (IBEenhcaf). 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.

Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review

“…This model has also been implemented for the crowd monitoring during Hajj. In [44], the authors have presented a framework for crowd behavior using an Enhanced Context-Aware Framework and achieved experimental results with the accuracy of 99.1% and 2.8% of False Negative Rate (FNR) indicating a significant improvement over the 92.0% accuracy and FNR of 31.3% of the Basic Context-Aware Framework (BCF). The detection of anomalous crowd behavior has been monitored in [45] using Isometric Mapping (ISOMAP).…”

Statistical Machine Learning for Human Behaviour Analysis

“…We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.…”

“…Four papers in this special issue have covered action recognition [ 6 , 9 , 12 , 13 ]. Reference [ 12 ] has proposed a system for toe-off detection using a regular camera.…”

“…The experimental results reported in [ 12 ] on public databases show state-of-the-art performance. Reference [ 6 ] proposes a system for individuals and then crowd condition monitoring and prediction. Individuals participating in this study are grouped into crowds based on their physical locations extracted using GPS on their smartphones.…”