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.