Electrical and Electronic Engineering - Research Publications
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ItemAn Improved Approach to Crowd Event Detection by Reducing Data Dimensions(SPRINGER-VERLAG BERLIN, 2016)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.
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ItemAnomalous Crowd Event Analysis Using Isometric Mapping(SPRINGER-VERLAG BERLIN, 2016)Anomalous event detection is one of the important applications in crowd monitoring. The detection of anomalous crowd events requires featurematrix to capture the spatio-temporal information to localize the events and detect the outliers. However, feature matrices often become computationally expensive with large number of features becomes critical for large-scale and real-time video analytics. In this work, we present a fast approach to detect anomalous crowd events and frames. First, to detect anomalous crowd events, the motion features are captured using the optical flow and a feature matrix of motion information is constructed and then subjected to nonlinear dimensionality reduction (NDR) using the Isometric Mapping (ISOMAP). Next, to detect anomalous crowd frames, the method uses four statistical features by dividing the frames into blocks and then calculating the statistical features for the blocks where objects were present. The main focus of this study is to understand the effect of large feature matrix size on detecting the anomalies with respect to computational time. Experiments were conducted on two datasets: (1) Performance Evaluation of Tracking and Surveillance (PETS) 2009 and (2) Melbourne Cricket Ground (MCG) 2011. Experiment results suggest that the ISOMAP NDR reduces the computation time significantly, more than ten times, to detect anomalous crowd events and frames. In addition, the experiment revealed that the ISOMAP provided an upper bound on the computational time.
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ItemInternet of Things for Structural Health Monitoring(CRC Press, 2016-05)The Internet revolution led to the interconnection between people at an unprecedented scale and pace. The ability of the sensor networks to send data over the Internet further enhanced the scope and usage of the sensor networks. The Internet uses unique address to identify the devices connected to the network. Structural Health Monitoring (SHM) implies monitoring of the state of the structures through sensor networks in an online mode and are pertinent to aircraft and buildings. SHM can be further divided into two categories: global health monitoring and local health monitoring. Continuous online SHM would be an ideal solution. SHM is performed by using acoustic sensors, ultrasonic sensors, strain gauges, optical fibers, and so on. Video cameras can also be used for SHM. SHM can be achieved in real-time and rich analytics. With the advent of smart sensors—sensors with programmable microprocessors, memory, and processing—has reduced load of central data processing, communication overhead while proving continuous SHM status.
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ItemAnomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features(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.
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ItemAutomatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-07)Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.
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ItemNon-Protruding Hazard Detection for the Aged Vision-Impaired(IEEE, 2016)Usage of the traditional white cane by the elderly with vision impairment is inefficient as many are also reliant on ambulatory aids such as wheelchairs and walking frames. The fall occurrence when using ambulatory aids is higher, contributed by non-protruding hazards such as potholes and drop-offs. Currently available technology for blind navigation, predominantly based on proximity sensing, is not designed to detect non protruding hazards. We address this critical need by developing a new optical laser system that combines innovative approaches in optical laser projection, vision-sensing, pattern recognition, and machine learning. Here, we present an overview of the system, including a new feature descriptor termed Histogram of Intersections, and results from our proof-of-concept demonstration.
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ItemA vision-based system to detect potholes and uneven surfaces for assisting blind people(IEEE, 2016)Vision is one of the most advanced and important sensory input in humans. However, many people have vision problems due to birth defects, uncorrected errors, work nature, accidents, and aging. The white cane and guide dog are the most widely used means of navigation for the vision-impaired. With advancements in technology, electronic devices have been created using different sensors and technologies to help navigate the blind. Electronic Travel AIDS (ETAs) assist in navigating a person by collecting information about the environment and relaying this information in a form that allows a blind or vision-impaired person to understand the nature of the environment. However, there is still a lack of devices to detect potholes and uneven pavements, which inhibits mobility after dark. This pilot study proposes a computer vision based pothole and uneven surface detection approach to assist blind people in meeting their mobility needs. The system includes projecting laser patterns, recording the patterns through a monocular video, analyzing the patterns to extract features and then providing path cues for the blind user. With over 90% accuracy in detecting potholes, the proposed system aims to assist blind people in real-time navigation.
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ItemCluster-based Crowd Movement Behavior Detection(IEEE, 2019-01-01)Crowd behaviour monitoring and prediction is an important research topic in video surveillance that has gained increasing attention. In this paper, we propose a novel architecture for crowd event detection, which comprises methods for object detection, clustering of various groups of objects, characterizing the movement patterns of the various groups of objects, detecting group events, and finding the change point of group events. In our proposed framework, we use clusters to represent the groups of objects/people present in the scene. We then extract the movement patterns of the various groups of objects over the video sequence to detect movement patterns. We define several crowd events and propose a methodology to detect the change point of the group events over time. We evaluated our scheme using six video sequences from benchmark datasets, which include events such as walking, running, global merging, local merging, global splitting and local splitting. We compared our scheme with state of the art methods and showed the superiority of our method in accurately detecting the crowd behavioral changes.
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ItemCrowd Event Detection on Optical Flow Manifolds(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-07)Analyzing crowd events in a video is key to understanding the behavioral characteristics of people (humans). Detecting crowd events in videos is challenging because of articulated human movements and occlusions. The aim of this paper is to detect the events in a probabilistic framework for automatically interpreting the visual crowd behavior. In this paper, crowd event detection and classification in optical flow manifolds (OFMs) are addressed. A new algorithm to detect walking and running events has been proposed, which uses optical flow vector lengths in OFMs. Furthermore, a new algorithm to detect merging and splitting events has been proposed, which uses Riemannian connections in the optical flow bundle (OFB). The longest vector from the OFB provides a key feature for distinguishing walking and running events. Using a Riemannian connection, the optical flow vectors are parallel transported to localize the crowd groups. The geodesic lengths among the groups provide a criterion for merging and splitting events. Dispersion and evacuation events are jointly modeled from the walking/running and merging/splitting events. Our results show that the proposed approach delivers a comparable model to detect crowd events. Using the performance evaluation of tracking and surveillance 2009 dataset, the proposed method is shown to produce the best results in merging, splitting, and dispersion events, and comparable results in walking, running, and evacuation events when compared with other methods.
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ItemArytenoid Cartilage Feature Point Detection Using Laryngeal 3D CT Images in Parkinson's Disease(IEEE, 2017-01-01)Parkinson's disease is a neurodegenerative disorder that results in progressive degeneration of nerve cells. It is generally associated with the deficiency of dopamine, a neurotransmitter involved in motor control of humans and thus affects the motor system. This results in abnormal vocal fold movements in majority of the Parkinson's patients. Analysis of vocal fold abnormalities may provide useful information to assess the progress of Parkinson's disease. This is accomplished by measuring the distance between the arytenoid cartilages during phonation. In order to automate this process of identifying arytenoid cartilages from CT images, in this work, a rule-based approach is proposed to detect the arytenoid cartilage feature points on either side of the airway. The proposed technique detects feature points by localizing the anterior commissure and analyzing airway boundary pixels to select the optimal feature point based on detected pixels. The proposed approach achieved 83.33% accuracy in estimating clinically-relevant feature points, making the approach suitable for automated feature point detection. To the best of our knowledge, this is the first such approach to detect arytenoid cartilage feature points using laryngeal 3D CT images.