Real-time Tracking System Research Articles

Cloud-Based Vehicle Tracking System

The need for vehicle tracking system in real time is growth continues due to increase the cases of theft. This type of system in real time needs to transmit large data with huge number of HTTP request to the server to keep tracking and monitoring in real time, thus causes spend extremely high cost every month for transportation the information on tracking vehicles to server therefor the needs for reducing the number of transportation and data size that transmits in each HTTP request to save expenses. This paper designed and implement an integrated vehicle tracking system in real time to track vehicle anywhere and anytime. This system is divided into two parts: vehicle tracking part and monitoring part. Tracking part is represented by installation the electronic devices in the vehicle using modern Global Positioning System (GPS), microcontroller Arduino UNO R3 and SIM800L GSM/GPRS modem. GPS is determined location of the vehicle via received coordinates from satellites such as latitude and latitude with accuracy ranging approximately 2.5 meters; the coordinates faked to add a type of protection to information on vehicles without effecting on characterizing real time tracking before sending it via a General Packet Radio service (GPRS). The monitoring part is in the cloud and will receive the coordinates and displays it on a map in a web page. The main contribution of this system is it reduced data size that sent from in-vehicle device via selected only necessary data for tracking vehicle from NEMA sentences of GPS and reduced number of HTTP request that sent to remote server via constrain the transmission of information with the movement of vehicles, since when vehicle moved the coordinates each 10s and did not send anything when the vehicle stopped thus will reduce the cost of expenses every month. This system can be utilized to track and monitoring the vehicles of large universities, companies, organization and also can be used in army vehicles and police vehicles.
 
 

Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

Sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this article, we propose a new method for reliably separating particles at nonuniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.

Real-Time Visibility and Tracking for Supply Chain Systems: Improving Inventory Management and Reducing Operational Costs Through Technology

Global supply chain systems are growing increasingly complex, and lately, the need for real-time visibility and tracking systems has intensified. This demonstrates the necessity of solutions that can help in managing inventory, reducing operational costs, and improved decision-making. The basis of this paper focuses on the role played by real-time tracking systems, that is, GPS, RFID, and IoT technologies, in enhancing supply chain visibility and efficiency. These technologies integrate to help the firms to react in advance to disruptions, track the goods in transit, and optimize supply chain operations. The paper also discusses the benefits, difficulties, and future prospects of real-time tracking by the firms in the modern supply chains while relevantly underlining the necessity of technological integration to meet the varying demands of the supply chain systems in this generation.

Face Detection using Support Vector Mechine with PCA

Face recognition is a popular subject in biometrics research which has distinct advantages because of its non-contact process. This technology has gained popularity because of its large application value and market value, like video surveillance system for real time tracking of suspicious object. In this paper we focus on the image face which has to be correctly recognized using support vector Machine (SVM) techniques with Principle Components Analysis (PCA) which extract the features and reduce dimensionality. Also we have used KNN classifier. The SVM with PCA produces more accurate result compare to other methods. This paper achieved 92% successful recognition rate for detecting different face databases.

Kinect-for-windows with augmented reality in an interactive roleplay system for children with an autism spectrum disorder

ABSTRACT Children with autism spectrum disorder (ASD) often find it difficult to communicate using body language and facial expressions to respond appropriately socially. This negatively affects the quality of their everyday social life. Children with ASD can learn to socially interact with virtual three-dimensional (3D) characters in animated games enhanced with augmented reality (AR). They can learn about body language and facial expressions through roleplay. This teaching method can effectively improve the interactive social skills of children with ASD and reduce the fear and anxiety which they typically experience when they face real people. However, most current virtual roleplay models focus only on predefined body language and facial expressions limited by the training system. These systems lack immediacy and a sense of genuine interaction for children with ASD. Therefore, we wanted to create a system that will allow trainers and therapists to control 3D virtual characters that are able to produce in real time the full range of natural body language and facial expressions appropriate for males, females, and even special cartoon characters. Based on the multiple baseline design across subjects, the data indicated that real-time Kinect Skeletal Tracking (KST) System can help the children with ASD improved their social interaction.

Dose Verification for Respiratory-gated VMAT-SBRT Using Real-time Tumor Tracking System

Recently, the introduction of various novel technologies in clinical settings has improved the accuracy of radiation therapy. Stereotactic body radiation therapy (SBRT) involves the delivery of an accurate radiation dose to the tumor with a minimal impact on normal tissues using various measures to address changes in the tumor position due to respiratory displacement. The SyncTraX FX4 real-time tumor tracking system (Shimadzu Corporation) introduced in our hospital tracks the actual tumor location by radioscopically monitoring a metallic marker that is placed in the vicinity of the tumor. However, there have been no reports yet on respiratory-gated volumetric modulated arc therapy (VMAT)-SBRT using a real-time tumor tracking system. This study aimed to develop an irradiation procedure for respiratory-gated VMAT-SBRT using a real-time tumor tracking system and to evaluate radiation doses therein. In this study, we found that absolute doses with respiratory gating did not deviate by more than ±1.0% from those without respiratory gating. In addition, the pass rate in gamma analysis using GAFCHROMIC EBT3 was ³95% with the pass criteria in dose difference, distance to agreement, and threshold being 2%, 2 mm, and 10%, respectively. Furthermore, a trajectory log file analysis did not reveal any significant error causes. Thus, these data indicate that respiratory-gated VMAT-SBRT can be applied clinically.

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Tracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach called Co-Tracking, a real-time target tracking system that leverages both citizens' mobile phones and stationary surveillance cameras to track moving objects collaboratively. Two key techniques are focused. Firstly, in order to accurately assign tracking tasks, we propose the Middle Query Location Prediction (MQLP) algorithm for predicting the target's location. Secondly, in order to efficiently utilizes these human/machine resources, we propose a heuristic algorithm, namely S-Maximum, to optimize the task allocation, including maximizing the number of completed tracking tasks and minimizing the number of mobile phones. Experimental results show that the proposed Co-Tracking system can effectively track moving objects with low incentive costs.

Real-Time Healthcare Monitoring System using Online Machine Learning and Spark Streaming

The real-time monitoring and tracking systems play a critical role in the healthcare field. Wearable medical devices with sensors, mobile applications, and health cloud have continuously generated an enormous amount of data, often called streaming big data. Due to the higher speed of the streaming data, it is difficult to ingest, process, and analyze such huge data in real-time to make real-time actions in case of emergencies. Using traditional methods that are inadequate and time-consuming. Therefore, there is a significant need for real-time big data stream processing to guarantee an effective and scalable solution. So, we proposed a new system for online prediction to predict health status using Spark streaming framework. The proposed system focuses on applying streaming machine learning models (i.e. streaming linear regression with SGD) on streaming health data events ingested to spark streaming through Kafka topics. The experimental results are done on the historical medical datasets (i.e. diabetes dataset, heart disease dataset, and breast cancer dataset) and generated dataset which is simulated to wearable medical sensors. The historical datasets have shown that the accuracy improvement ratio obtained using the diabetes disease dataset is the highest one with respect to the other two datasets with an accuracy of 81%. For generated datasets, the online prediction system has achieved accuracy with 98% at 5 seconds window size. Beyond this, the experimental results have proofed that the online prediction system can online learn and update the model according to the new data arrival and window size.

Emotion-preserving Blendshape Update with Real-time Face Tracking

Blendshape representations are widely used in facial animation. Consistent semantics must be maintained for all the blendshapes to build the blendshapes of one character. However, this is difficult for real characters because the face shape of the same semantics varies significantly across identities. Previous studies have handled this issue by asking users to perform a set of predefined expressions with specified semantics. We observe that facial emotions can be used to define semantics. Herein, we propose a real-time technique that directly updates blendshapes without predefined expressions. Its aim is to preserve semantics based on the emotion information extracted from an arbitrary facial motion sequence. In addition, we have designed corresponding algorithms to efficiently update blendshapes with large- and middle-scale face shapes and fine-scale facial details, such as wrinkles, in a real-time face tracking system. The experimental results indicate that using a commodity RGBD sensor, we can achieve real-time online blendshape updates with well-preserved semantics and user-specific facial features and details.

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

Lane departure warning systems have gained considerable research interest in the past decade for its promising usage in automotive, where lane detection and tracking is applied. However, it is a challenging task to improve the robustness of lane detection due to environmental factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues, we propose a reliable vision-based real-time lane markings detection and tracking system that can adapt to various environmental conditions. The lane detection is composed of three stages: pre-processing, Adaptive Region of Interest (AROI) setting, and lane marking detection and tracking. In the pre-processing stage, smoothing and edge detection operators are applied on input frames to automatically obtain binary images, then, lane markings segmentation are carried out. After that, An Adaptive Region of Interest is extracted to reduce the computational complexity. In the subsequent detection stages, Kalman filter is employed to track road boundaries detected in the AROI using Progressive Probabilistic Hough Transform (PPHT) in the next frame. Based on road boundaries and the vehicle's position, the proposed algorithm decides if the vehicle has drifted off the lane. For the performance evaluation of lane detection and tracking, real-life datasets for both urban roads and highways in various lighting conditions are used. Applying our method to Catltech dataset, the average correct detection rate is 93.82%. In addition, the proposed method outperforms that of the state-of-the-art methods in processing time (21.54ms/frame).

LimbMotion

Wearable-based human-computer interaction is a promising technology to enable various applications. This paper aims to track the 3D posture of the entire limb, both wrist/ankle and elbow/knee, of a user wearing a smart device. This limb tracking technology can trace the geometric motion of the limb, without introducing any training stage usually required in gesture recognition approaches. Nonetheless, the tracked limb motion can also be used as a generic input for gesture-based applications. The 3D posture of a limb is defined by the relative positions among main joints, e.g., shoulder, elbow, and wrist for an arm. When a smartwatch is worn on the wrist of a user, its position is affected by both elbow and shoulder motions. It is challenging to infer the entire 3D posture when only given a single point of sensor data from the smartwatch. In this paper, we propose LimbMotion, an accurate and real-time limb tracking system. The performance gain of LimbMotion comes from multiple key technologies, including an accurate attitude estimator based on a novel two-step filter, fast acoustic ranging, and point clouds-based positioning. We implemented LimbMotion and evaluated its performance using extensive experiments, including different gestures, moving speeds, users, and limbs. Results show that LimbMotion achieves real-time tracking with a median error of 7.5cm to 8.9cm, which outperforms the state-of-the-art approach by about 32%.

Use of a novel internet-based real-time patient tracking system in a mock mass casualty incident

Myers, Brett MPAP, MSPH, PA-C; Goldberg, Alana PA-C, MPAP, MPH; Fenn, Peter DHSc, MPAS, PA-C Author Information

21 Performance assessment of the Calypso® system

Introduction. The Calypso® 4D localization system (Calypso® Medical Technologies, Seattle, Wa) is a real-time electromagnetic tracking system. It is used to continuously monitor implanted Beacon® transponders for patient alignment and target position tracking during the treatment. The purpose of the study was to assess the performances, the accuracy and the stability of this system.

A new Internet of Things architecture for real-time prediction of various diseases using machine learning on big data environment

A number of technologies enabled by Internet of Thing (IoT) have been used for the prevention of various chronic diseases, continuous and real-time tracking system is a particularly important one. Wearable medical devices with sensor, health cloud and mobile applications have continuously generating a huge amount of data which is often called as streaming big data. Due to the higher speed of the data generation, it is difficult to collect, process and analyze such massive data in real-time in order to perform real-time actions in case of emergencies and extracting hidden value. using traditional methods which are limited and time-consuming. Therefore, there is a significant need to real-time big data stream processing to ensure an effective and scalable solution. In order to overcome this issue, this work proposes a new architecture for real-time health status prediction and analytics system using big data technologies. The system focus on applying distributed machine learning model on streaming health data events ingested to Spark streaming through Kafka topics. Firstly, we transform the standard decision tree (DT) (C4.5) algorithm into a parallel, distributed, scalable and fast DT using Spark instead of Hadoop MapReduce which becomes limited for real-time computing. Secondly, this model is applied to streaming data coming from distributed sources of various diseases to predict health status. Based on several input attributes, the system predicts health status, send an alert message to care providers and store the details in a distributed database to perform health data analytics and stream reporting. We measure the performance of Spark DT against traditional machine learning tools including Weka. Finally, performance evaluation parameters such as throughput and execution time are calculated to show the effectiveness of the proposed architecture. The experimental results show that the proposed system is able to effectively process and predict real-time and massive amount of medical data enabled by IoT from distributed and various diseases.

Research on digital production technology for traditional manufacturing enterprises based on industrial Internet of Things in 5G era

Based on the analysis of 5G and Internet of Things technology, this paper proposes the reference architecture of smart factory and its application path for traditional manufacturing enterprises in China, in which the intelligent manufacturing workshop is the core component of smart factory. The Internet of Things technology combined the advanced technologies (Industrial Big Data, WSN, RFID, Cloud Computing Platform) and provides hardware network foundation and technical theory for designing the real-time tracking and monitoring system of intelligent workshop products. The developed system has the advantages of low cost, rapid deployment, and convenient expansion, which traditional manufacturing enterprises realize intelligent management based on IoT application platform.

Controlling a motorized electric wheelchair based on face tilting

Abstract Disability, specifically impaired upper and/or lower limbs, has a direct impact on the patients’ quality of life. Nowadays, motorized wheelchairs supported by a mobility-aided technique have been devised to improve the quality of life of these patients by increasing their independence. This study aims to present a platform to control a motorized wheelchair based on face tilting. A real-time tracking system of face tilting using a webcam and a microcontroller circuit has been designed and implemented. The designed system is dedicated to control the movement directions of the motorized wheelchair. Four commands were adequate to perform the required movements for the motorized wheelchair (forward, right, and left, as well as stopping status). The platform showed an excellent performance regarding controlling the motorized wheelchair using face tilting, and the position of the eyes was shown as the most useful face feature to track face tilting.

Design of Beidou Satellite System in Ocean Logistics Real-Time Tracking System

Hao, Q.; Wang, Z.-F. and Qin, L.-L., 2019. Design of Beidou satellite system in ocean logistics real-time tracking system. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 204–207. Coconut Creek (Florida), ISSN 0749-0208.In order to ensure the quality and safety of pelagic fish catches in the process of transportation, administrator needs to carry out real-time monitoring and management to the transport and quality status during this period and establish a complete management system. This paper puts forward that the real-time online monitoring of transport location, catches temperature, humidity, PH value and other physical property data can be used during the application process of Beidou System and RFID temperature tags in the cold chain logistics of ocean catches. The system consists of 3 parts, i.e. vehicle or vessel tracking, parameter monitoring and data management and analysis. The system applies the technologies such as Beidou, ZigBee and Internet as well as the Java-programming client web page structure and the traditional SSH2 as its code framework. Hence, it realizes the real-time monitoring of whole catches transportation logistics process and makes administrator timely know about the status of catches so as to ensure the product quality. This system can guarantee the quality, safety and transport efficiency of pelagic fish products and provides a support for the building of cold chain logistics system.

Interactive Hierarchical Object Proposals

Object proposal algorithms have been demonstrated to be very successful in accelerating object detection process. High object localization quality and detection recall can be obtained using thousands of proposals. However, the performance with a small number of proposals is still unsatisfactory. This paper demonstrates that the performance of a few proposals can be significantly improved with the minimal human interaction—a single touch point. To this end, we first generate hierarchical superpixels using an efficient tree-organized structure as our initial object proposals, and then select only a few proposals from them by learning an effective Convolutional neural network for objectness ranking. We explore and design an architecture to integrate human interaction with the global information of the whole image for objectness scoring, which is able to significantly improve the performance with a minimum number of object proposals. Extensive experiments show the proposed method outperforms all the state-of-the-art methods for locating the meaningful object with the touch point constraint. Furthermore, the proposed method is extended for video. By combining with the novel interactive motion segmentation cue for generating hierarchical superpixels, the performance on a single proposal is satisfactory and can be used in the interactive vision systems, such as selecting the input of a real-time tracking system.

Real-time spatial intersecting seam tracking based on laser vision stereo sensor

A real-time robotic weld tracking system based on laser vision sensor is designed for intersecting seam. Firstly, the traditional image processing method is used to determine the weld feature point in the first frame when there has no arc noise. Then, an extending and adopting Kalman Filter with Spatio-Temporal Context algorithm was proposed to extract the weld feature point when the laser stripe was blocked by heavy arc and splash noise during welding. Next, in order to control torch to weld automatically, the torch frame was established through novel three points principle and an expanding circle and arc length method. In addition, a segmentation weld method and an intermediate proportional interpolation method for step-size control of mobile torch were proposed to guarantee the accurate of weld. Finally, Experiments and analyses results show that the tracking system is good in real-time, accuracy, stability and flexible, which can meet the weld requirements.

Real-time visual detection and tracking system for traffic monitoring

Computer vision systems for traffic monitoring represent an essential tool for a broad range of traffic surveillance applications. Two of the most noteworthy challenges for these systems are the real-time operation with hundreds of vehicles and the total occlusions which hinder the tracking of the vehicles. In this paper, we present a traffic monitoring approach that deals with these two challenges based on three modules: detection, tracking and data association. First, vehicles are identified through a deep learning based detector. Second, tracking is performed with a combination of a Discriminative Correlation Filter and a Kalman Filter. This permits to estimate the tracking error in order to make tracking more robust and reliable. Finally, the data association through the Hungarian algorithm combines the information of the previous steps. The contributions are: (i) a real-time traffic monitoring system robust to occlusions that can process more than four hundred vehicles simultaneously; and (ii) the application of the system to anomaly detection in traffic and roundabout input/output analysis. The system has been evaluated with more than two thousand vehicles in real-life videos.