A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video

Anomalous indications in monitoring equipment on board U.S. Navy vessels must be handled in a timely manner to prevent catastrophic system failure. The development of sensor data analysis techniques to assist a ship\u27s crew in monitoring machinery and summon required ship-to-shore assistance is of considerable benefit to the Navy. In addition, the Navy has a large interest in the development of distance support technology in its ongoing efforts to reduce manning on ships. In this thesis, algorithms have been developed for the detection of anomalous events that can be identified from the analysis of monochromatic stationary ship surveillance video streams. The specific anomalies that we have focused on are the presence and growth of smoke and fire events inside the frames of the video stream. The algorithm consists of the following steps. First, a foreground segmentation algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced using morphological operations. Following this step, the anomalous indication is tracked between frames using Kalman filtering. Finally, gray level shape and motion features corresponding to the anomaly are subjected to principal component analysis and classified using a multilayer perceptron neural network. The algorithm is exercised on 68 video streams that include the presence of anomalous events (such as fire and smoke) and benign/nuisance events (such as humans walking the field of view). Initial results show that the algorithm is successful in detecting anomalies in video streams, and is suitable for application in shipboard environments

Detecting Small Moving Targets in Infrared Imagery

Deep convolutional neural networks have achieved remarkable results for detecting large and medium sized objects in images. However, the ability to detect smallobjects has yet to achieve the same level performance. Our focus is on applications that require the accurate detection and localization of small moving objects that are distantfrom the sensor. We first examine the ability of several state-of-the-art object detection networks (YOLOv3 and Mask R-CNN) to find small moving targets in infraredimagery using a publicly released dataset by the US Army Night Vision and Electronic Sensors Directorate. We then introduce a novel Moving Target Indicator Network (MTINet) and repurpose a hyperspectral imagery anomaly detection algorithm, the Reed-Xiaoli detector, for detecting small moving targets. We analyze the robustness and applicability of these methods by introducing simulated sensor movement to the data. Compared with other state-of-the-art methods, MTINet and the ARX algorithm achieve ahigher probability of detection at lower false alarm rates. Specifically, the combination of the algorithms results in a probability of detection of approximately 70% at a low false alarm rate of 0.1, which is about 50% greater than that of YOLOv3 and 65% greater than Mask R-CNN

Machine Learning Methods for Autonomous Flame Detection in Videos

Fire detection has attracted increasing attention from the public because of the huge loss caused by fires every year. Compared with the traditional fire detection techniques based on smoke or heat sensors, the frameworks using machine learning methods in videos for fire detection have the advantages of higher efficiency and accuracy of detection, robustness to various environments, and lower cost of the systems. The uniqueness of these frameworks stems from the developed machine learning approaches for autonomous information extraction and fire detection in sequential video frames. A framework for flame detection is proposed based on the synergy of the Horn-Schunck optical flow estimation method, a probabilistic saliency analysis approach and a temporal wavelet analysis scheme. The estimated optical flows, together with the saliency analysis method, work effectively in selecting moving regions by well describing the dynamic property of flames, which contributes to accurate detection of flames. Additionally, the temporal wavelet transform based analysis increases the robustness of the framework and provides reliable results by discarding non-flame pixels according to their temporally changing patterns. Apart from the dynamic characteristic of flames, the property of colours is also of crucial importance in describing flames. However, the colours of flames usually vary significantly with different illumination or burning material, which results in a wide diversity. To well model the various colours, a novel flame colour model is proposed based on the Dirichlet process Gaussian mixture model. The distribution of flame colours is represented by a Gaussian mixture model, of which the number of mixture components is learned from the training data autonomously by setting a Dirichlet process as the prior. Compared with those methods which set the number of mixture components empirically, the developed model can access a more accurate estimation of the distribution of flame colours. The inference is successfully implemented by two methods, i.e., the Gibbs sampling and variational inference algorithms, to manage different quantities of training data. The colour model can be incorporated into the framework of flame detection and the results show that the colour model achieves a highly accurate estimation of the distribution of flame colours, which contributes to the good performance of the whole framework. All the proposed approaches are tested on real videos of various environments and proved to be capable of accurate flame detection

Automatic Fire Detection Using Computer Vision Techniques for UAV-based Forest Fire Surveillance

Due to their rapid response capability and maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potentials for forest fire surveillance and detection. Over the last decade, it has shown an increasingly strong demand for UAV-based forest fire detection systems, as they can avoid many drawbacks of other forest fire detection systems based on satellites, manned aerial vehicles, and ground equipments. Despite this, the existing UAV-based forest fire detection systems still possess numerous practical issues for their use in operational conditions. In particular, the successful forest fire detection remains difficult, given highly complicated and non-structured environments of forest, smoke blocking the fire, motion of cameras mounted on UAVs, and analogues of flame characteristics. These adverse effects can seriously cause either false alarms or alarm failures. In order to successfully execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, investigations on how to reduce false alarm rates, increase the probability of successful detection, and enhance adaptive capabilities to various circumstances are strongly demanded to improve the reliability and accuracy of forest fire detection system. According to the above-mentioned requirements, this thesis concentrates on the development of reliable and accurate forest fire detection algorithms which are applicable to UAVs. These algorithms provide a number of contributions, which include: (1) a two-layered forest fire detection method is designed considering both color and motion features of fire; it is expected to greatly improve the forest fire detection performance, while significantly reduce the motion of background caused by the movement of UAV; (2) a forest fire detection scheme is devised combining both visual and infrared images for increasing the accuracy and reliability of forest fire alarms; and (3) a learning-based fire detection approach is developed for distinguishing smoke (which is widely considered as an early signal of fire) from other analogues and achieving early stage fire detection

JUNO Conceptual Design Report

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.Comment: 328 pages, 211 figure

State-of-the-Art Sensors Technology in Spain 2015: Volume 1

This book provides a comprehensive overview of state-of-the-art sensors technology in specific leading areas. Industrial researchers, engineers and professionals can find information on the most advanced technologies and developments, together with data processing. Further research covers specific devices and technologies that capture and distribute data to be processed by applying dedicated techniques or procedures, which is where sensors play the most important role. The book provides insights and solutions for different problems covering a broad spectrum of possibilities, thanks to a set of applications and solutions based on sensory technologies. Topics include: • Signal analysis for spectral power • 3D precise measurements • Electromagnetic propagation • Drugs detection • e-health environments based on social sensor networks • Robots in wireless environments, navigation, teleoperation, object grasping, demining • Wireless sensor networks • Industrial IoT • Insights in smart cities • Voice recognition • FPGA interfaces • Flight mill device for measurements on insects • Optical systems: UV, LEDs, lasers, fiber optics • Machine vision • Power dissipation • Liquid level in fuel tanks • Parabolic solar tracker • Force sensors • Control for a twin roto

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Advanced Processing of Multispectral Satellite Data for Detecting and Learning Knowledge-based Features of Planetary Surface Anomalies

abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201