Electrical and Electronic Engineering - Research Publications
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ItemDetection of anomalous crowd behaviour using hyperspherical clusteringRao, AS ; Gubbi, J ; Rajasegarar, S ; Marusic, S ; Palaniswami, M (IEEE, 2015-01-12)Analysis of crowd behaviour in public places is an indispensable tool for video surveillance. Automated detection of anomalous crowd behaviour is a critical problem with the increase in human population. Anomalous events may include a person loitering about a place for unusual amounts of time; people running and causing panic; the size of a group of people growing over time etc. In this work, to detect anomalous events and objects, two types of feature coding has been proposed: spatial features and spatio-temporal features. Spatial features comprises of contrast, correlation, energy and homogeneity, which are derived from Gray Level Co-occurrence Matrix (GLCM). Spatio-temporal feature includes the time spent by an object at different locations in the scene. Hyperspherical clustering has been employed to detect the anomalies. Spatial features revealed the anomalous frames by using contrast and homogeneity measures. Loitering behaviour of the people were detected as anomalous objects using the spatio-temporal coding.
ItemAnomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal FeaturesYang, M ; Rajasegarar, S ; Rao, AS ; Leckie, C ; Palaniswami, M ; Shi, Z ; Vadera, S ; Li, G (Springer, 2016)important problem in real-life applications. Detection of anomalous behaviors such as people standing statically and loitering around a place are the focus of this paper. In order to detect anomalous events and objects, ViBe was used for background modeling and object detection at first. Then, a Kalman filter and Hungarian cost algorithm were implemented for tracking and generating trajectories of people. Next, spatio-temporal features were extracted and represented. Finally, hyperspherical clustering was used for anomaly detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio-temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real-life video surveillance environment.
ItemReal-Time Urban Microclimate Analysis Using Internet of ThingsRathore, P ; Rao, AS ; Rajasegarar, S ; Vanz, E ; Gubbi, J ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018-04-01)Real-time environment monitoring and analysis is an important research area of Internet of Things (IoT). Understanding the behavior of the complex ecosystem requires analysis of detailed observations of an environment over a range of different conditions. One such example in urban areas includes the study of tree canopy cover over the microclimate environment using heterogeneous sensor data. There are several challenges that need to be addressed, such as obtaining reliable and detailed observations over monitoring area, detecting unusual events from data, and visualizing events in real-time in a way that is easily understandable by the end users (e.g., city councils). In this regard, we propose an integrated geovisualization framework, built for real-time wireless sensor network data on the synergy of computational intelligence and visual methods, to analyze complex patterns of urban microclimate. A Bayesian maximum entropy-based method and a hyperellipsoidal model-based algorithm have been build in our integrated framework to address above challenges. The proposed integrated framework was verified using the dataset from an indoor and two outdoor network of IoT devices deployed at two strategically selected locations in Melbourne, Australia. The data from these deployments are used for evaluation and demonstration of these components' functionality along with the designed interactive visualization components.
ItemA visual-numeric approach to clustering and anomaly detection for trajectory dataKumar, D ; Bezdek, JC ; Rajasegarar, S ; Leckie, C ; Palaniswami, M (SPRINGER, 2017-03-01)
ItemCentered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor NetworksRajasegarar, S ; Leckie, C ; Bezdek, JC ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2010-09-01)