scholarly journals Challenges of an Autonomous Wildfire Geolocation System Based on Synthetic Vision Technology

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3631 ◽  
Author(s):  
Victor Arana-Pulido ◽  
Francisco Cabrera-Almeida ◽  
Javier Perez-Mato ◽  
B. Dorta-Naranjo ◽  
Silvia Hernandez-Rodriguez ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Tracking System ◽  
High Volume ◽  
Human Interaction ◽  
Detection Methods ◽  
Synthetic Vision ◽  
Technological Advantage ◽  
Wildfire Suppression ◽  
Dry Conditions

Thermographic imaging has been the preferred technology for the detection and tracking of wildfires for many years. Thermographic cameras provide some very important advantages, such as the ability to remotely detect hotspots which could potentially turn into wildfires if the appropriate conditions are met. Also, they can serve as a key preventive method, especially when the 30-30-30 rule is met, which describes a situation where the ambient temperature is higher than 30 ∘ C, the relative humidity is lower than 30%, and the wind speed is higher than 30 km/h. Under these circumstances, the likelihood of a wildfire outburst is quite high, and its effects can be catastrophic due to the high-speed winds and dry conditions. If this sort of scenario actually occurs, every possible technological advantage shall be used by firefighting teams to enable the rapid and efficient coordination of their response teams and to control the wildfire following a safe and well-planned strategy. However, most of the early detection methods for wildfires, such as the aforementioned thermographic cameras, lack a sufficient level of automation and usually rely on human interaction, imposing high degrees of subjectivity and latency. This is especially critical when a high volume of data is required in real time to correctly support decision-making scenarios during the wildfire suppression tasks. The present paper addresses this situation by analyzing the challenges faced by a fully autonomous wildfire detection and a tracking system containing a fully automated wildfire georeferencing system based on synthetic vision technology. Such a tool would provide firefighting teams with a solution capable of continuously surveilling a particular area and completely autonomously identifying and providing georeferenced information on current or potential wildfires in real time.

REAL-TIME FACIAL POSE IDENTIFICATION WITH HIERARCHICALLY STRUCTURED ML POSE CLASSIFIER

2004 ◽  
Vol 18 (02) ◽  
pp. 127-142 ◽  
Author(s):  
JAE-WON SUNG ◽  
DAIJIN KIM
Keyword(s):  
Real Time ◽  
High Speed ◽  
Tracking System ◽  
Computation Time ◽  
Feature Space ◽  
Identification Problem ◽  
Pose Tracking ◽  
Time Simulation ◽  
The Face ◽  
Pose Identification

Since pose-varying face images form nonlinear convex manifold in high dimensional image space, it is difficult to model their pose distribution in terms of a simple probabilistic density function. To solve this difficulty, we divide the pose space into many constituent pose classes and treat the continuous pose estimation problem as a discrete pose-class identification problem. We propose to use a hierarchically structured ML (Maximum Likelihood) pose classifiers in the reduced feature space to decrease the computation time for pose identification, where pose space is divided into several pose groups and each group consists of a number of similar neighboring poses. We use the CONDENSATION algorithm to find a newly appearing face and track the face with a variety of poses in real-time. Simulation results show that our proposed pose identification using the hierarchically structured ML pose classifiers can perform a faster pose identification than conventional pose identification using the flat structured ML pose classifiers. A real-time facial pose tracking system is built with high speed hierarchically structured ML pose classifiers.

scholarly journals A Deep Learning Streaming Methodology for Trajectory Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 250
Author(s):  
Ioannis Kontopoulos ◽  
Antonios Makris ◽  
Konstantinos Tserpes
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Frequency ◽  
Tracking System ◽  
High Volume ◽  
Classification Performance ◽  
Automatic Identification ◽  
Mobility Patterns ◽  
Real World Data ◽  
Vessel Tracking

Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking system that transmits their location periodically. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. The automatic identification of vessel mobility patterns from such data in real time is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Therefore, in this work, we present a novel approach that transforms streaming vessel trajectory patterns into images and employs deep learning algorithms to accurately classify vessel activities in near real time tackling the Big Data challenges of volume and velocity. Two real-world data sets collected from terrestrial, vessel-tracking receivers were used to evaluate the proposed methodology in terms of both classification and streaming execution performance. Experimental results demonstrated that the vessel activity classification performance can reach an accuracy of over 96% while achieving sub-second latencies in streaming execution performance.

scholarly journals High-Sensitivity Real-Time Tracking System for High-Speed Pipeline Inspection Gauge

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 731 ◽  
Author(s):  
Guanyu Piao ◽  
Jingbo Guo ◽  
Tiehua Hu ◽  
Yiming Deng
Keyword(s):  
Real Time ◽  
High Speed ◽  
Tracking System ◽  
High Sensitivity ◽  
Decay Rates ◽  
Least Square ◽  
Physical Parameters ◽  
Burial Depth ◽  
Pipeline Inspection ◽  
Real Time Tracking

Real-time tracking of pipeline inspection gauges (PIGs) is an important aspect of ensuring the safety of oil and gas pipeline inline inspections (ILIs). Transmitting and receiving extremely low frequency (ELF) magnetic signals is one of the preferred methods of tracking. Due to the increase in physical parameters of the pipeline including transportation speed, wall thickness and burial depth, the ELF magnetic signals received are short transient (1-second duration) and very weak (10 pT), making the existing above-ground-marker (AGM) systems difficult to operate correctly. Based on the short transient very weak characteristics of ELF signals studied with a 2-D finite-element method (FEM) simulation, a data fusion model was derived to fuse the envelope decay rates of ELF signals by a least square (LS) criterion. Then, a fast-decision-tree (FDT) method is proposed to estimate the fused envelope decay rate to output the maximized orthogonal signal power for the signal detection through a determined topology and a fast calculation process, which was demonstrated to have excellent real-time detection performance. We show that simulation and experimental results validated the effectiveness of the proposed FDT method, and describe the high-sensitivity detection and real-time implementation of a high-speed PIG tracking system, including a transmitter, a receiver, and a pair of orthogonal search coil sensors.

Advanced Intervention Technology Platform Provides Increased Capability Enabling Efficient High-Volume Wellbore Cleanout Solutions on Electric Line

2021 ◽  
Author(s):  
Maša Brankovic ◽  
Stuart William Murchie ◽  
Odd Einar Magnussen ◽  
Espen Osaland ◽  
Niels Erik Sørensen ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Effective Means ◽  
Drill Pipe ◽  
High Volume ◽  
Team Approach ◽  
Small Component ◽  
Technology Platform ◽  
Well Completion ◽  
Electric Line

Abstract Electric line deployed well intervention technologies are continuing to grow in use and relevance, this is due to the value provided by a highly efficient and effective means of intervention. It is light on equipment, personnel and logistics, is controlled and precise in its in-well execution, and is less obtrusive to the wellbore, the reservoir and the environment. These valuable characteristics are making electric line deployed solutions the preferred choice of customers for many interventions, whether that be for addressing new well completion, old well rejuvenation or repair, or eventual plug and abandon (P&A) operational scope. Preference is also increasing with those customers who are keen to push the boundaries of these technologies to leverage their beneficial impact across a broader range of intervention operations. Often, the tasks or workscope assigned to electric line deployed intervention technologies are reserved for what would be considered the lighter end of the spectrum, for example, low volume debris cleanout, small component milling and low force tool manipulation. However, as full system-based intervention technology platforms are developed, incorporating advanced interconnected technology components, the magnitude of what can be achieved has expanded electric line intervention solutions into the realms of work scope previously reserved for heavier methods, like coiled tubing or drill pipe based. That, coupled with the efficient and precise execution and inherently light footprint that electric line deployed intervention brings, is adding to the increased interest in expanding its use. Two recent electric line deployed wellbore cleanout operations carried out on the Norwegian Continental Shelf involving high volumes of debris demonstrate the advantages this advanced intervention technology platform has enabled, the scalability of its performance, and why it is challenging the traditional thinking and perception of what is possible on electric line. In the case operation 1, extensive volumes of produced sand had accumulated in a large mono-bore completion preventing the execution of a required P&A operation. In case operation 2, the well had significant Barium Sulphate (BaSO4) scale deposits over much of its length, which prevented well access for a required gas lift valve (GLV) change out. In both these cases, efficient and effective electric line deployed remediation was possible due to the increased performance, in-well task visibility and real-time task control provided by the advanced electric line intervention technology platform that was utilised. Attempting both these cases was strongly encouraged by the customer, leading a one team approach. For case operation 1, high speed tractor conveyance speeds of over 26 meters/minute were achieved on this multi-run operation. Instrumentation provided real-time indication of collection chambers being full, ensuring minimal time on depth during collection. Consistent high volume recovery rates of 100% were achieved on all but one of the collection runs, with a total of 1400 liters of sand debris being collected, clearing 280 meters of wellbore, at an average of 140 liters per 24 hours. For case operation 2, over 2000 meters of hard scale was milled, at a rate of penetration (ROP) of 44 meters/hour, on average, re-establishing access for required electric line intervention runs and the subsequent change out of the leaking GLV to restore the integrity of the well, enabling it to be put back on line and resume production. Record breaking achievements regarding the volume of debris removed and depth intervals cleaned via the intervention technology platform were made in both cases.

scholarly journals Real Time Face Recognition and Tracking System with Multi-Camera Arrangement

2020 ◽  
Vol 9 (4) ◽  
pp. 1247-1251
Keyword(s):  
Face Recognition ◽  
Object Tracking ◽  
Real Time ◽  
Data Storage ◽  
Video Processing ◽  
High Speed ◽  
Tracking System ◽  
Traffic Monitoring ◽  
Multiple Cameras ◽  
The Face

Object tracking and face reorganization has received tremendous attention in the video processing community due to its various applications in video surveillance, traffic monitoring and so on. A single camera is not capable to scan 3d view of specified space. So, we use multiple cameras, placed in different sections of the area with overlapping region in field of view (FOV). Every camera will capture the video scene of itself FOV. The system is able to track human successfully by setting up correspondence between objects captured in multiple cameras. Thus, it saves the hectic job of manual tracking. There is a search window available for each object that gives the object’s trajectory. Tracking of object will be given by continuation of this process. For monitoring objects in areas like car parking, banks, hotels etc for security purpose, this system is best. Over the last couple of years, many algorithms and results have been presented for the problem of object tracking and recently the focus has been concentrated on real time person tracking with multiple cameras. Secondly, face detection is one of the best ways of identification. The main applications of automated face recognition are of biometric authentications and surveillances. Face recognition systems has became popular in biometric field as it is non intrusive and does not require the human interference. Up to that, there is no solution or technique that provides robust methods. This paper presents the detection of the face of the person, recognize and do tracking with use of multiple web cameras. Generally in daily camera security systems, cameras have been continuously remain on and large data storage is required in the system. In this real time object tracking system, Infrared sensors are used which indicates presence of person or object. Cameras will turn on only when object is detected by sensor after then the face recognition is carried out. It has capability of high speed processing and achieved low computational requirements. In similar areas, efficiency, accuracy, and speed of identification are the main tackled issues.

scholarly journals Toward Real-time Microseismic Event Detection using the YOLOv3 Algorithm

2022 ◽  
Author(s):  
Xiaoyu Zhu ◽  
Jeffrey Shragge
Keyword(s):  
Real Time ◽  
Event Detection ◽  
High Speed ◽  
Detection System ◽  
Gaussian White Noise ◽  
Detection Methods ◽  
Support Vector ◽  
Signal To Noise ◽  
Microseismic Events ◽  
Microseismic Event

Real-time microseismic monitoring is essential for understanding fractures associated with underground fluid injection in unconventional reservoirs. However, microseismic events recorded on monitoring arrays are usually contaminated with strong noise. With a low signal-to-noise ratio (S/R), the detection of microseismic events is challenging using conventional detection methods such as the short-term average/long-term average (STA/LTA) technique. Common machine learning methods, e.g., feature extraction plus support vector machine (SVM) and convolutional neural networks (CNNs), can achieve higher accuracy with strong noise, but they are usually time-consuming and memory-intensive to run. We propose the use of YOLOv3, a state-of-art real-time object detection system in microseismic event detection. YOLOv3 is a one-stage deep CNN detector that predicts class confidence and bounding boxes for images at high speed and with great precision. With pre-trained weights from the ImageNet 1000-class competition dataset, physics-based training of the YOLOv3 algorithm is performed on a group of forward modeled synthetic microseismic data with varying S/R. We also add randomized forward-modeled surface seismic events and Gaussian white noise to generate ``semi-realistic'' training and testing datasets. YOLOv3 is able to detect weaker microseismic event signals with low signal-to-noise ratios (e.g., S/N=0.1) and achieves a mean average precision of 88.71\% in near real time. Further work is required to test YOLOv3 in field production settings.

High speed interferometric device for real-time correction of aero-optic effects

1995 ◽  
Author(s):  
Rod Clark ◽  
John Karpinsky ◽  
Gregg Borek ◽  
Eric Johnson
Keyword(s):  
Real Time ◽  
High Speed ◽  
Time Correction
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