SIMCD: SIMulated crowd data for anomaly detection and prediction

Smart Crowd management (SCM) solutions can mitigate overcrowding disasters by implementing efficient crowd learning models that can anticipate critical crowd conditions and potential catastrophes. Developing an SCM solution involves monitoring crowds and modelling their dynamics. Crowd monitoring produces vast amounts of data, with features such as densities and speeds, which are essential for training and evaluating crowd learning models. By and large, crowd datasets can be classified as real (e.g., real monitoring of crowds) or synthetic (e.g., simulation of crowds). Using real crowd datasets can produce effective and reliable crowd learning models. However, acquiring real crowd data faces several challenges, including the expensive installation of a sensory infrastructure, the data pre-processing costs and the lack of real datasets that cover particular crowd scenarios. Consequently, crowd management literature has adopted simulation tools for generating synthetic datasets to overcome the challenges associated with their real counterparts. The majority of existing datasets, whether real or synthetic, can be used for crowd counting applications or analysing the activities of individuals rather than collective crowd behaviour. Accordingly, this paper demonstrates the process of generating bespoke synthetic crowd datasets that can be used for crowd anomaly detection and prediction, using the MassMotion crowd simulator. The developed datasets present two types of crowd anomalies; namely, high densities and contra-flow walking direction. These datasets are: SIMulated Crowd Data (SIMCD)-Single Anomaly and SIMCD-Multiple Anomalies for anomaly detection tasks, besides two SIMCD-Prediction datasets for crowd prediction tasks. Furthermore, the paper demonstrates the data preparation (pre-processing) process by aggregating the data and proposing new essential features, such as the level of crowdedness and the crowd severity level, that are useful for developing crowd prediction and anomaly detection models

SIMCD: SIMulated crowd data for anomaly detection and prediction

Smart Crowd management (SCM) solutions can mitigate overcrowding disasters by implementing efficient crowd learning models that can anticipate critical crowd conditions and potential catastrophes. Developing an SCM solution involves monitoring crowds and modelling their dynamics. Crowd monitoring produces vast amounts of data, with features such as densities and speeds, which are essential for training and evaluating crowd learning models. By and large, crowd datasets can be classified as real (e.g., real monitoring of crowds) or synthetic (e.g., simulation of crowds). Using real crowd datasets can produce effective and reliable crowd learning models. However, acquiring real crowd data faces several challenges, including the expensive installation of a sensory infrastructure, the data pre-processing costs and the lack of real datasets that cover particular crowd scenarios. Consequently, crowd management literature has adopted simulation tools for generating synthetic datasets to overcome the challenges associated with their real counterparts. The majority of existing datasets, whether real or synthetic, can be used for crowd counting applications or analysing the activities of individuals rather than collective crowd behaviour. Accordingly, this paper demonstrates the process of generating bespoke synthetic crowd datasets that can be used for crowd anomaly detection and prediction, using the MassMotion crowd simulator. The developed datasets present two types of crowd anomalies; namely, high densities and contra-flow walking direction. These datasets are: SIMulated Crowd Data (SIMCD)-Single Anomaly and SIMCD-Multiple Anomalies for anomaly detection tasks, besides two SIMCD-Prediction datasets for crowd prediction tasks. Furthermore, the paper demonstrates the data preparation (pre-processing) process by aggregating the data and proposing new essential features, such as the level of crowdedness and the crowd severity level, that are useful for developing crowd prediction and anomaly detection models

Machine Learning Algorithms for Privacy-preserving Behavioral Data Analytics

PhD thesisBehavioral patterns observed in data generated by mobile and wearable devices are used by many applications, such as wellness monitoring or service personalization. However, sensitive information may be inferred from these data when they are shared with cloud-based services. In this thesis, we propose machine learning algorithms for data transformations to allow the inference of information required for specific tasks while preventing the inference of privacy-sensitive information. Specifically, we focus on protecting the user’s privacy when sharing motion-sensor data and web-browsing histories. Firstly, for human activity recognition using data of wearable sensors, we introduce two algorithms for training deep neural networks to transform motion-sensor data, focusing on two objectives: (i) to prevent the inference of privacy-sensitive activities (e.g. smoking or drinking), and (ii) to protect user’s sensitive attributes (e.g. gender) and prevent the re-identification of user. We show how to combine these two algorithms and propose a compound architecture that protects both sensitive activities and attributes. Alongside the algorithmic contributions, we published a motion-sensor dataset for human activity recognition. Secondly, to prevent the identification of users using their web-browsing behavior, we introduce an algorithm for privacy-preserving collaborative training of contextual bandit algorithms. The proposed method improves the accuracy of personalized recommendation agents that run locally on the user’s devices. We propose an encoding algorithm for the user’s web-browsing data that preserves the required information for the personalization of the future contents while ensuring differential privacy for the participants in collaborative training. In addition, for processing multivariate sensor data, we show how to make neural network architectures adaptive to dynamic sampling rate and sensor selection. This allows handling situations in human activity recognition where the dimensions of input data can be varied at inference time. Specifically, we introduce a customized pooling layer for neural networks and propose a customized training procedure to generalize over a large number of feasible data dimensions. Using the proposed architectural improvement, we show how to convert existing non-adaptive deep neural networks into an adaptive network while keeping the same classification accuracy. We conclude this thesis by discussing open questions and the potential future directions for continuing research in this area

ANOMALY DETECTION in CROWDS USING MULTI SENSORY INFORMATION

This paper presents, a system capable of detecting unusual activities in crowds from real-world data captured from multiple sensors. The detection is achieved by classifying the distinct movements of people in crowds, and those patterns can be different and can be classified as normal and abnormal activities. Statistical features are extracted from the dataset collected by applying sliding time window operations. A model for classifying movements is trained by using Random Forest technique. The system was tested by using two datasets collected from mobile phones during social events gathering. Results show that mobile data can be used to detect anomalies in crowds as an alternative to video sensors with significant performances. Our approach is the first to detect any unusual behaviour in crowd with non-visual data, which is simple to train and easy to deploy. We also present our dataset for public research as there is no such dataset available to perform experiments on crowds for detecting unusual behaviours