Electrical and Electronic Engineering - Research Publications
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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.
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)
ItemQuarter Sphere Based Distributed Anomaly Detection in Wireless Sensor NetworksRajasegarar, S ; LECKIE, C ; PALANISWAMI, M ; Bezdek, J (IEEE - Institute of Electrical and Electronic Engineers, 2007)
ItemElliptical Anomalies in Wireless Sensor NetworksRajasegarar, S ; Bezdek, JC ; Leckie, C ; Palaniswami, M (ASSOC COMPUTING MACHINERY, 2009-12-01)Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an important task in managing these types of networks. A key challenge is how to detect anomalies with few false alarms while preserving the limited energy in the network. In this article, we define different types of anomalies that occur in wireless sensor networks and provide formal models for them. We illustrate the model using statistical parameters on a dataset gathered from a real wireless sensor network deployment at the Intel Berkeley Research Laboratory. Our experiments with a novel distributed anomaly detection algorithm show that it can detect elliptical anomalies with exactly the same accuracy as that of a centralized scheme, while achieving a significant reduction in energy consumption in the network. Finally, we demonstrate that our model compares favorably to four other well-known schemes on four datasets.
ItemAnomaly detection in wireless sensor networksRajasegarar, S ; Leckie, C ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2008-08-01)
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)