Abstract
The increase of emergency situations that cause mass panic in mass gatherings, such as terrorist attacks, random shooting, stampede, and fires, sheds light on the fact that advancements in technology should contribute in timely detecting and reporting serious crowd abnormal behaviour. The new paradigm of the ‘Internet of Things’ (IoT) can contribute to that. In this study, a method for real-time detection of abnormal crowd behaviour in mass gatherings is proposed. This system is based on advanced wireless connections, wearable sensors and machine learning technologies. It is a new crowdsourcing approach that considers humans themselves as the surveillance devices that exist everywhere. A sufficient number of the event’s attendees are supposed to wear an electronic wristband which contains a heart rate sensor, motion sensors and an assisted-GPS, and has a wireless connection. It detects the abnormal behaviour by detecting heart rate increase and abnormal motion. Due to the unavailability of public bio-dataset on mass panic, dataset of this study was collected from 89 subjects wearing the above-mentioned wristband and generating 1054 data samples. Two types of data collected were: firstly, the data of normal daily activities and secondly, the data of abnormal activities resembling the behaviour of escape panic. Moreover, another abnormal dataset was synthetically generated to simulate panic with limited motion. In our proposed approach, two-phases of data analysis are done. Phase-I is a deep machine learning model that was used to analyze the sensors’ collected readings of the wristband and detect if the person has indeed panicked in order to send alerting signals. While phase-II data analysis takes place in the monitoring server that receives the alerting signals to conclude if it is a mass panic incident or a false positive case. Our experiments demonstrate that the proposed system can offer a reliable, accurate, and fast solution for panic detection. This experiment uses the Hajj pilgrimage as a case study.
Highlights
The increase of emergency situations cause mass panic, such as terrorist attacks, random shooting, stampede, natural catastrophes, and fires requires fast detection and swift action to save lives
We found that using sequence labeling methods such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) can capture the temporal and spatial relationship between the Heart Rate (HR) and movement readings, compared to non-sequential models like Support Vector Machine (SVM) and Vanilla Neural Network (VNN)
Our results show that LSTM performed the best, followed by GRU, while SVM and VNN achieved less accuracy
Summary
The increase of emergency situations cause mass panic, such as terrorist attacks, random shooting, stampede, natural catastrophes, and fires requires fast detection and swift action to save lives. Detection of an incident will give the emergency authorities valuable time to deal with the situation and prevent it from getting worse by implementing immediate and possibly automated actions. Mass gatherings are exposed to unpleasant incidents due to the large number of people and limited space and exit routes. Mobile Crowd Sensing (MCS) is a new sensing paradigm based on collecting real-time data from two participatory sources: sensing and social media platforms. It allows ordinary users to contribute by sharing real-time data with data sensed or collected from their mobile devices/wearables. The revolution of cost-effective hardware, emergent computing and communication trends such as the Internet of Things (IoT), big data, machine learning, wearable sensing and cloud computing have enabled the existence of mobile crowd sensing applications and made our environment smarter
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