Electrical and Electronic Engineering - Research Publications

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    Energy Efficient Time Synchronization in WSN for Critical Infrastructure Monitoring
    Rao, AS ; Gubbi, J ; Tuan, N ; Nguyen, J ; Palaniswami, M ; Wyld, DC ; Wozniak, M ; Chaki, N ; Meghanathan, N ; Nagamalai, D (SPRINGER-VERLAG BERLIN, 2011-01-01)
    Wireless Sensor Networks (WSN) based Structural Health Monitoring (SHM) is becoming popular in analyzing the life of critical infrastructure such as bridges on a continuous basis. For most of the applications, data aggregation requires high sampling rate. A need for accurate time synchronization in the order of 0.6 − 9 μs every few minutes is necessary for data collection and analysis. Two-stage energy-efficient time synchronization is proposed in this paper. Firstly, the network is divided into clusters and a head node is elected using Low-Energy Adaptive Clustering Hierarchy based algorithm. Later, multiple packets of different lengths are used to estimate the delay between the elected head and the entire network hierarchically at different levels. Algorithmic scheme limits error to 3-hop worst case synchronization error. Unlike earlier energy-efficient time synchronization schemes, the achieved results increase the lifetime of the network.
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    A robust algorithm for foreground extraction in crowded scenes
    Rao, AS ; Gubbi, J ; Marusic, S ; Palaniswami, M (IEEE, 2012-12-01)
    The widespread availability of surveillance cameras and digital technology has improved video based security measures in public places. Surveillance systems have been assisting officials both in civil and military applications. It is helping to identify unlawful activities by means of uninterrupted transmission of surveillance videos. By this, the system is adding extraneous onus on to the already existing workload of security officers. Instead, if the surveillance system is intelligent and efficient enough to identify the events of interest and alert the officers, it alleviates the burden of continuous monitoring. In other words, our existing surveillance systems are lacking to identify the objects that are dissimilar in shape, size, and color especially in identifying human beings (nonrigid motions). Global illumination changes, frequent occurrences of shadows, insufficient lighting conditions, unique properties of slow and fast moving objects, unforeseen appearance of objects and its behavior, availability of system memory, etc., may be ascribed to the limitations of existing systems. In this paper, we present a filtering technique to extract foreground information, which uses RGB component and chrominance channels to neutralize the effects of nonuniform illumination, remove shadows, and detect both slow-moving and distant objects.
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    A Pilot Study of Urban Noise Monitoring Architecture Using Wireless Sensor Networks
    Gubbi, J ; Marusic, S ; Rao, AS ; Law, YW ; Palaniswami, M (IEEE, 2013-01-01)
    Internet of Things (IoT) is denned as interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications. As the world urban population is set to cross unprecedented levels, adequate provision of services and infrastructure poses huge challenges. The emerging IoT that offers ubiquitous sensing and actuation can be utilized effectively for managing urban environments. In this paper, a new architecture for noise monitoring in urban environments is proposed. The architecture is scalable and applicable to other sensors required for city management. In addition to the architecture, a new noise monitoring hardware platform is reported and visualization of the data is presented. An emerging citizen centric participatory sensing is discussed in the context of noise monitoring.
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    A Pilot Study on the use of Accelerometer Sensors for Monitoring Post Acute Stroke Patients
    Gubbi, J ; Kumar, D ; Rao, AS ; Yan, B ; Palaniswami, M (IEEE, 2013-01-01)
    The high incidence of stroke has raised a major concern among health professionals in recent years. Concerted efforts from medical and engineering communities are being exercised to tackle the problem at its early stage. In this direction, a pilot study to analyze and detect the affected arm of the stroke patient based on hand movements is presented. The premise is that the correlation of magnitude of the activities of the two arms vary significantly for stroke patients from controls. Further, the cross-correlation of right and left arms for three axes are differentiable for patients and controls. A total of 22 subjects (15 patients and 7 controls) were included in this study. An overall accuracy of 95.45% was obtained with sensitivity of 1 and specificity of 0.86 using correlation based method.
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    Crowd Density Estimation Based on Optical Flow and Hierarchical Clustering
    Rao, AS ; Gubbi, J ; Marusic, S ; Stanley, P ; Palaniswami, M (IEEE, 2013-01-01)
    Crowd density estimation has gained much attention from researchers recently due to availability of low cost cameras and communication bandwidth. In video surveillance applications, counting people and creating a temporal profile is of high interest. Surveillance systems face difficulties in detecting motion from the scene due to varying environmental conditions and occlusion. Instead of detecting and tracking individual person, density estimation is an approximate method to count people. The approximation is often more accurate than individual tracking in occluded scenarios. In this work, a new technique to estimate crowd density is proposed. A block-based dense optical flow with spatial and temporal filtering is used to obtain velocities in order to infer the locations of objects in crowded scenarios. Furthermore, a hierarchical clustering is employed to cluster the objects based on Euclidean distance metric. The Cophenetic correlation coefficient for the clusters highlighted the fact that our preprocessing and localizing of object movements form hierarchical clusters that are structured well with reasonable accuracy without temporal post-processing.
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    Determination of Object Directions Using Optical Flow for Crowd Monitoring
    Rao, AS ; Gubbi, J ; Marusic, S ; Maher, A ; Palaniswami, M ; Bebis, G ; Boyle, R ; Parvin, B ; Koracin, D ; Li, B ; Porikli, F ; Zordan, V ; Klosowski, J ; Coquillart, S ; Luo, X ; Chen, M ; Gotz, D (SPRINGER-VERLAG BERLIN, 2013-01-01)
    Determination of object direction in a multi-camera tracking system is critical. The absence of object direction from other cameras pose challenges if the object is along the optical axis. The problem of determining object direction worsens further if the cameras in the existing infrastructure are improperly placed and are uncontrollable. To determine the direction of an object in such situations, three methods based on optical flow (OF) are presented. The first method uses centroids of optical flow vector magnitudes and Kalman filter for tracking and is suitable for less crowded scenarios. The second method uses geometric moments to evaluate the flow vector distribution and to ascertain the direction in case of crowded scenarios by partitioning the scene and then applying moments to individual partitions independently. The third method is appropriate for small-sized objects near vanishing points where global object motion is less. During surveillance, whether multi-object, single-object or crowded scenarios, the aforementioned methods are applicable accordingly. The results show that the object directions can be accurately inferred from three methods for different scenarios.
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    Probabilistic detection of crowd events on riemannian manifolds
    Rao, AS ; Gubbi, J ; Marusic, S ; Palaniswami, M (IEEE, 2015-01-12)
    Event detection in crowded scenarios becomes complex due to articulated human movements, occlusions and complexities involved in tracking individual humans. In this work, we focus on crowd event (activity) detection and classification. We focus on active crowd (continuously moving crowd) events. First, event primitives such as motion, action, activity and behaviour are defined. Furthermore, a distinction is made among event detection, action recognition and abnormal event detection. Further, event detection and classification are defined on Riemannian Manifolds that yields six different probabilities of the event occurring. Using a new probabilistic approach, an automated event detection algorithm is proposed that temporally segments the event using a novel framework. The results indicate that the proposed approach delivers superior performance in selected cases and similar results in other cases, whilst the detection model delay allows operation in near real-time. The Performance Evaluation of Tracking and Surveillance (PETS) 2009 dataset was used for evaluation. Existing crowd event detection approaches used supervised approach, whereas we eschew semi-supervised approach.
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    Detection of anomalous crowd behaviour using hyperspherical clustering
    Rao, 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.
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    Classification of Convulsive Psychogenic Non-epileptic Seizures Using Histogram of Oriented Motion of Accelerometry Signals
    Kusmakar, S ; Gubbi, J ; Rao, AS ; Yan, B ; O'Brien, TJ ; PALANISWAMI, M (IEEE, 2015)
    A seizure is caused due to sudden surge of electrical activity within the brain. There is another class of seizures called psychogenic non-epileptic seizure (PNES) that mimics epilepsy, but is caused due to underlying psychology. The diagnosis of PNES is done using video-electroencephalography monitoring (VEM), which is a resource intensive process. Recently, accelerometers have been shown to be effective in classification of epileptic and non-epileptic seizures. In this work, we propose a novel feature called histogram of oriented motion (HOOM) extracted from accelerometer signals for classification of convulsive PNES. An automated algorithm based on HOOM is proposed. The algorithm showed a high sensitivity of (93.33%) and an overall accuracy of (80%) in classifying convulsive PNES.
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    An Improved Approach to Crowd Event Detection by Reducing Data Dimensions
    Rao, AS ; Gubbi, J ; Palaniswami, M ; Thampi, SM ; Bandyopadhyay, S ; Krishnan, S ; Li, KC ; Mosin, S ; Ma, M (SPRINGER-VERLAG BERLIN, 2016-01-01)
    Crowd monitoring is a critical application in video surveillance. Crowd events such as running, walking, merging, splitting, dispersion, and evacuation inform crowd management about the behavior of groups of people. For an effective crowd management, detection of crowd events provides an early sign of the behavior of the people. However, crowd event detection using videos is a highly challenging task because of several challenges such as non-rigid human body motions, occlusions, unavailability of distinguishing features due to occlusions, unpredictability in people movements, and other. In addition, the video itself is a high-dimensional data and analyzing to detect events becomes further complicated. One way of tackling the huge volume of video data is to represent a video using low-dimensional equivalent. However, reducing the video data size needs to consider the complex data structure and events embedded in a video. To this extent, we focus on detection of crowd events using the Isometric Mapping (ISOMAP) and Support Vector Machine (SVM). The ISOMAP is used to construct the low-dimensional representation of the feature vectors, and then an SVM is used for training and classification. The proposed approach uses Haar wavelets to extract Gray Level Coefficient Matrix (GLCM). Later, the approach extracts four statistical features (contrast, correlating, energy, and homogeneity) at different levels of Haar wavelet decomposition. Experiment results suggest that the proposed approach is shown to perform better when compared with existing approaches.