Real-time Tracking System Research Articles

Real-Time Dynamic Intelligent Image Recognition and Tracking System for Rockfall Disasters.

Taiwan, frequently affected by extreme weather causing phenomena such as earthquakes and typhoons, faces a high incidence of rockfall disasters due to its largely mountainous terrain. These disasters have led to numerous casualties, government compensation cases, and significant transportation safety impacts. According to the National Science and Technology Center for Disaster Reduction records from 2010 to 2022, 421 out of 866 soil and rock disasters occurred in eastern Taiwan, causing traffic disruptions due to rockfalls. Since traditional sensors of disaster detectors only record changes after a rockfall, there is no system in place to detect rockfalls as they occur. To combat this, a rockfall detection and tracking system using deep learning and image processing technology was developed. This system includes a real-time image tracking and recognition system that integrates YOLO and image processing technology. It was trained on a self-collected dataset of 2490 high-resolution RGB images. The system's performance was evaluated on 30 videos featuring various rockfall scenarios. It achieved a mean Average Precision (mAP50) of 0.845 and mAP50-95 of 0.41, with a processing time of 125 ms. Tested on advanced hardware, the system proves effective in quickly tracking and identifying hazardous rockfalls, offering a significant advancement in disaster management and prevention.

Smart River Information Services in managing Nile River navigation system

Abstract In terms of the significance of incorporating smart techniques into large river navigation systems to increase inland navigation competitiveness, this article introduces the development of smart River Information Services (RIS) in Egypt based on smart information and communication technologies as real-time tracking systems, Open Geographic Information Systems, and Nile River Information Systems, as well as proposes measures to tackle the challenges that may be faced, compared with experiences and knowledge of RIS in large river smart navigation systems. This comparison creates new frameworks for the future development of using smart technology in inland waterways in Egypt. It could be concluded that advanced navigation technologies and infrastructures are still being developed in Egypt and are not yet available. Almost all of Egypt's Vessel Traffic Services station equipment and applications are imported from Europe. Undoubtedly, there will be at least a 5- to 10-year time lag for Egypt to develop independent RIS to its full extent. It is necessary to keep good cooperation with the European and Chinese inland sector and refer to their experience and knowledge.

The Role of Ai in Transforming Smes: Opportunities and Challenges

Artificial Intelligence (AI) systems assist in enhancing data analysis with meaningful insights and promote real-time decision construction that implements blockchain technology in data security. This factor guides SMEs in managing big data by usage of predictive analysis and algorithms in reducing operational complexity in the USA. There are 33.2 million SMEs in the US and contribute 50% of the overall GDP in this country as per the 2023 report [1]. The application of AI and automation technology leads to improved work distribution and enhanced overall performance in handling the SME's services. This factor encouraged operational facilities to use the real-time tracking system and promote the level of the smart manufacturing process. The primary research used 101 participants to conduct a survey on SME employees for the transformation of SMEs with AI in analyzing opportunities and challenges. This analysis reflects that most of the employees felt encouraged to adopt AI and blockchain in expanding the overall productivity of USA SMEs.

Robust self-learning autofocus system for real-time 4D nanoparticle tracking with digital holographic microscope

Robust self-learning autofocus system for real-time 4D nanoparticle tracking with digital holographic microscope

Vehicle Tracking System for Small Scale Areas Using Wireless Sensor Network

A real-time vehicle tracking system is designed for specific covering zones based on wireless sensor network (WSN) technology. In this designed WSN infrastructure the location of the vehicles is tracked using a Neo-6MV2 UBLOX GPS module which is placed on the vehicles. Furthermore, the Enhanced ShockBurst protocol is employed to transmit wirelessly acquired location data to a master/center (destination) node using the NRF24101+PA LNA modules. The location information of the vehicle is graphically represented for a security center using an ADO.net-based desktop application. It is used to record the vehicle location information at certain time intervals (minutes) in the MSSQL database. This system intimates the system administrator about the long ques/waiting time of vehicles outside the premeditated area. After the successful deployment of the proposed WSN system, it has been substantiated that the proposed system can be used as an alternative to other wireless network infrastructures like WIFI or GSM for the small-scale areas vehicle tracking.

Spatiotemporal prediction of particulate matter concentration based on traffic and meteorological data

Air pollution threatens worldwide human health, ecosystems, and climate change. Transportation is a major contributor to air pollution. However, the link between transportation and air pollution is intricate and influenced by multiple elements. This study employs spatiotemporal causal convolutional networks to predict air pollutants by utilizing traffic and meteorological data as inputs. Using Seoul, South Korea as a case study, a dataset of 25 regional air monitoring stations is used for prediction. The results confirm that wind speed and direction significantly impact PM2.5 dispersion, while humidity positively correlates with PM2.5 concentrations and temperature shows an inverse relationship. Additionally, vehicular traffic and subway passenger numbers exhibit positive associations, attributed to automotive emissions, road dust resuspension, and heightened human activity near subway stations, respectively. The model can potentially be utilized in a real-time air pollution tracking system to facilitate prompt interventions and reduce the harmful effects of air contamination on public health.

3-D real-time ultrasound tracking of acoustically actuated swimming microdrone

Maneuverable microswimmers/microdrones that navigate in hard-to-reach spaces inside human bodies hold a great potential for various biomedical applications. Acoustically actuated microswimmers have already demonstrated feasibility. However, for eventual translation of this technology, a robust 3-D tracking strategy for the microswimmer is particularly required. This paper presents our lab-designed 3-D ultrasound tracking system for real-time tracking of an acoustically actuated 3-D swimming microdrone. The ultrasound tracking system utilizing two ultrasound probes, a step motor and a host controller, was built to track the 3-D arbitrary motion of the microdrone in real-time. The performance of tracking was evaluated in the benchtop experiments by comparing the reconstructed trajectories with synchronized camera recordings. The ultrasound tracking system showed high reliability, with an average error of less than 0.3 mm across six different trials when compared to camera tracking. The results demonstrated the capability of our lab-designed 3-D ultrasound tracking system in accurately tracking the undetermined motion of the acoustic actuated 3-D swimming microdrone in real-time. The developed tracking system holds promise as a potential approach for biomedical applications and could pave the way for future clinical translation of the microswimmer technology.

A real-time seam tracking system based on extraction and restoration model in robotic GMAW

A real-time seam tracking system based on extraction and restoration model in robotic GMAW

Real-time Tracking System for Distribution Information of Logistics Enterprises Based on IOT Technology

Logistics enterprise distribution information real-time tracking system in the actual application process, there is the problem of low cargo throughput, this study was aimed to design a kind of logistics enterprise distribution information real-time tracking system based on Internet of Things technology. In hardware part: control the reading and writing equipment according to the predetermined working mode, adjust the parameters of the reader, connect the circuit and other accessories; in software part: increase the correlation between the tables by setting foreign keys, build the logistics enterprise distribution information database, control and supervise the logistics activities. The result shows that the average value of cargo throughput between the real-time tracking system of distribution information of logistics enterprises in the paper and two other real-time tracking systems of logistics enterprises is 1412.14 T/H, 1049.38 T/H and 1088.81 T/H respectively, indicating that the real-time tracking system of distribution information of logistics enterprises designed after the application of IOT technology is more perfect.

Real-time Soldier Health and Location Tracking System

To improve military personnel's overall health and operational efficiency, the Soldier's Health Monitoring and Position Tracking System integrates innovative technology in a comprehensive way. Military commanders are guaranteed complete situational awareness with this system's combination of real-time health monitoring and exact position tracking. Vital physiological indicators including heart rate, temperature, and blood oxygen levels are continuously collected and analysed as part of the health monitoring element. The measurements are sent wirelessly to a command centre, so any possible health problems may be identified quickly and responded to quickly. In order to keep meticulous records of each soldier's exact whereabouts in the field, the system simultaneously makes use of cutting-edge GPS and INS technology. The seamless integration of geo-spatial information with health data gives commanders a comprehensive picture of the troops' physical state and where they are. Military authorities may make educated judgements based on the collective health and positioning data using the system's user interface, which provides an intuitive dashboard for real-time monitoring. The data will be sent to the command centre via the Internet of Things. Components of the suggested system include gearbox modules, small wearable physiological equipment, and sensors. As a result, the suggested gear may be used to provide a cheap method of protecting the precious human life on the battlefield. Using global positioning system (GPS) and cellular service mobile phone (GSM) technology, this device can wirelessly monitor the soldier's vitals, including his or her heart rate and temperature.

Automatic Lane Discovery and Traffic Congestion Detection in a Real-Time Multi-Vehicle Tracking Systems

Automatic Lane Discovery and Traffic Congestion Detection in a Real-Time Multi-Vehicle Tracking Systems

A Real Time Face Recognition and Tracking Framework Using Lightweight Convolutional Neural Network

Human face recognition and tracking (FRT) plays a vital role in various fields, including security, authentication, and human-computer interaction. The main modules of the FRT system are detection, feature extraction, and FRT. Using a database, these units recognize faces as well as their location, movement, and visible features. The framework aims to process large visual data in real-time, enabling accurate and fast FRT. The paper develops a real-time FRT framework using a lightweight CNN convolutional neural network to accurately match images of faces and environments with different illumination and expression differences to improve performance. This paper focused on the development of real-time facial recognition and tracking systems. The model used to achieve this is based on deep learning (DL) using a lightweight convolutional neural network (CNN) and post-feature extraction using linear discriminant analysis (LDA). Histogram of Oriented Gradients (HOG) experiments demonstrate that DL with lightweight CNN models provides a good solution for FRT tasks, even in challenging situations including changes in position, expression, illumination, and occlusion. The results of CNN-based DL were compared with several experiments. The model was also compared with many modern methods and achieved better results. The lightweight CNN model for DL outperformed it 100% of the time. When the split rate is 70:30 and the learning rate is 0.001, the epoch is 100. This demonstrates the dominance of DL over other techniques and shows how well it handles FRT tasks using lightweight and even real-time CNN methods.

Enhancing Solar Energy Generation: A Comprehensive Machine Learning-Based PV Prediction and Fault Analysis System for Real-Time Tracking and Forecasting

This paper addresses a notable gap in the field of photovoltaic system forecasting by introducing the Machine Learning-based PV Prediction and Fault Analysis System (ML-PVPFAS). This framework is designed to optimize the decomposition of variational systems automatically, using a multi-objective intelligent optimization method to establish its weight. The paper evaluates a range of Machine Learning (ML) and Ensemble Learning (EL) techniques for diagnosing faults in PV arrays, with a primary focus on identifying and categorizing complex faults that could affect these arrays. These faults encompass multiple anomalies and defects with similar current-voltage curves, which have not been previously examined. The analysis of prediction scores reveals that the ML-PVPFAS approach outperforms other methods, with the lowest Mean Absolute Percentage Error (MAPE) at 4.93, Root Mean Squared Error (RMSE) at 4.64, high Accuracy at 90.31%, Precision at 93.60%, a strong Pearson Correlation Coefficient of 0.90, and a fast Computation Time of 77.64 milliseconds. The results suggest that ML-PVPFAS is a dependable and practical algorithm for predicting the power output of PV solar systems, making it a valuable contribution to the field of predictive modeling.

Real-Time Object Detection and Tracking for Unmanned Aerial Vehicles Based on Convolutional Neural Networks

This paper presents a system applied to unmanned aerial vehicles based on Robot Operating Systems (ROSs). The study addresses the challenges of efficient object detection and real-time target tracking for unmanned aerial vehicles. The system utilizes a pruned YOLOv4 architecture for fast object detection and the SiamMask model for continuous target tracking. A Proportional Integral Derivative (PID) module adjusts the flight attitude, enabling stable target tracking automatically in indoor and outdoor environments. The contributions of this work include exploring the feasibility of pruning existing models systematically to construct a real-time detection and tracking system for drone control with very limited computational resources. Experiments validate the system’s feasibility, demonstrating efficient object detection, accurate target tracking, and effective attitude control. This ROS-based system contributes to advancing UAV technology in real-world environments.

A horizontal single-axis tracking bracket with an adjustable tilt angle and its adaptive real-time tracking system for bifacial PV modules

An efficient photovoltaic (PV) tracking system enables solar cells to produce more energy. However, commonly-used PV tracking systems experience the following limitations: (ⅰ) they are mainly applied to single-sided PV panels; (ⅱ) they employ conventional astronomical algorithms that cannot adjust the tracking path in real time according to variable weather. In this study, a model of horizontal single-axis tracking bracket with an adjustable tilt angle (HSATBATA) is developed, and the irradiance model of moving bifacial PV modules is designed, which considers the mounting height, spacing and ground shading of PV panels. Furthermore, an adaptive real-time tracking (ARTT) algorithm is put forward to obtain the optimal tracking path for PV cells, which considers the energy consumption of tracking motors, the front and back irradiance of solar cells, cell temperature and ambient wind speed. The test results indicate that the presented ARTT algorithm enhances the energy of PV modules by 32.7 % and 7.5 %, respectively, compared to the fixed bracket and the conventional tracking algorithm. Additionally, the number of motor starts of the PV tracking system is reduced by 71.7 % compared with that of the conventional algorithm, which greatly contributes to extending the service life of PV tracking brackets and lowering the cost of electricity. Present study will help to improve the theoretical research system of PV tracking bracket construction, irradiance modeling of moving bifacial modules, and intelligent tracking algorithms.

Women Safety Device with Shock Taser and Location Tracking

Abstract: This abstract provides an overview of a Women Safety Device. The safety and security of women continue to be a significant concern in modern society. To address this issue, we present a comprehensive Women Safety Device equipped with a Shock Taser and Location Tracking, implemented using the Arduino Uno microcontroller. The core functionalities of the proposed device include a shock taser for self- defense and a location tracking system to ensure quick response in case of emergencies. The shock taser is designed to deliver a non-lethal electric shock, acting as a deterrent against potential threats. The device utilizes high-voltage, low-current pulses generated by the Arduino Uno to create a safe and effective self-defense mechanism. Furthermore, the location tracking feature utilizes GPS and GSM technologies to continuously monitor the user's real-time position. The Arduino Uno gathers GPS data and transmits it via GSM to the predefined contacts. This allows authorized contacts, such as family members or authorities, to track the user's location and respond promptly to distress situations. By combining a shock taser for self-defense and a real-time location tracking system, this device contributes to a safer environment for women and provides peace of mind for both users and their loved ones.

Sistem Informasi Monitoring Pengiriman Kargo DAMRI Logistik Lintas Jawa - Sumatera Berbasis Web

Cargo delivery is an integral part of logistics operations, where monitoring plays a crucial role. Manual approaches in monitoring increase the risk of human error and prolong the process duration. Major issues frequently encountered in cargo delivery, such as damages, losses, and bus travel delays, can be addressed through the implementation of monitoring systems. Damages and losses during cargo transportation, coupled with significant bus travel delays, stem from finalizing cargo delivery data after passenger check-in. This research aims to identify the root causes of these three issues and offer solution recommendations to enhance the reliability of the cargo delivery process. The research methodology includes analysis of historical delivery data, interviews with operational crews, and the development of a web-based information system for more structured monitoring processes. Research findings indicate that discrepancies between delivery schedules and inconsistent monitoring are primary factors contributing to damages, losses, and delays. Recommended solutions involve implementing real-time tracking systems and providing training to enhance awareness of swift and accurate delivery procedures. In conclusion, understanding the challenges encountered in cargo delivery can contribute to developing better service solutions, thereby improving overall logistics efficiency within DAMRI logistics.

A Real-Time Vessel Detection and Tracking System Based on LiDAR.

Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking.

Miniaturized high-resolution dual 2D MEMS mirror scanning confocal Raman microscopy for topographic and Raman mapping

A novel dual 2D MEMS mirror scanning confocal Raman microscopy (D-2D-MEMS-CRM) method is proposed to realize the miniaturized design of the confocal Raman microscopy. This method uses two 2D MEMS mirrors to realize beam telecentric scanning, which eliminates the relay system consisting of a scan lens and tube lens, significantly shortens the optical length of the system, and improves the detection efficiency of Raman signal. Moreover, this method uses reflected light to construct a real-time focus tracking system, which improves the spatial resolution of the confocal Raman detection system. Based on this method, a miniaturized D-2D-MEMS-CRM system was established, which had an axial focusing capability of 50 nm using an objective lens with NA of 0.6, and the lateral resolution of 500 nm for both topographic and Raman mapping. In addition, the system achieved simultaneous topographic imaging and Raman mapping of carbonaceous chondrite, demonstrating its effectiveness in extraterrestrial sample detection.

Real-time tracking and berthing aid system with occlusion handling based on LiDAR

In smart ports, accurate perception of the state of a berthing ship relative to the berth is critical for the safety and efficiency of ships and ports. A real-time tracking and berthing system with occlusion handling based on Light Detection and Ranging (LiDAR) is proposed. First, the data is pre-processed, including fast filtering and clustering, and then a real-time tracking framework is proposed to solve the occlusion problem. This framework adapts to different berthing scenarios through accurate determination of dynamic and static targets and efficient retracking of berthing ships. To verify the effectiveness of the method, an on-site monitoring experiment of the “Yukun” training ship in Dalian Port is conducted, and a simulated berthing scene under occlusion is constructed to test the algorithm. The practical results prove that our method shows feasibility and effectiveness in the application of ship dynamic target tracking and safe ship berthing.