Real-time Tracking System Research Articles

Design of efficient single target real-time beam tracking system

This paper builds a system for real-time tracking of a single target Radio Frequency (RF) signal source, in which the transmitter uses a low-cost HackRF One software radio transceiver platform, and the receiving antenna array uses two monopole antennas to form a 1×2 receiving array. The receiver uses Xilinx 7-Series development board Zedboard and AD9361 RF transceiver to build a wireless receiving platform to process the received signal. In this RF beam tracking system, it needs to perform Direction of Arrival (DOA) estimation on the calibrated signal, the DOA algorithm plays a crucial role in the accuracy of beam pointing and system running time, the MUSIC algorithm takes some time to conduct spectrum peak search, so the Root-Music algorithm is used here to avoid the spectrum peak search problem. To enable the Root-MUSIC algorithm to be better executed on this two-element receiving platform, this work simplifies the entire calculation process, requiring only a small amount of calculation and running time to complete the DOA estimation step, which effectively save FPGA processing time and computing resources. It provides a guarantee for the real-time performance of the system.

Magnetic Tracking System for Heart Surgery.

Cardiac ablation is a minimally invasive, low risk procedure that can correct heart rhythm problems. Current techniques which determine catheter positioning while a patient is undergoing heart surgery are usually invasive, often inaccurate, and require some forms of imaging. In this study, we develop a unique real-time tracking system which can track the position and orientation of a medical catheter inside a human heart with fast update rate of 200 Hz and high precision of 1.6 mm. The system utilizes a magnetic field-based positioning method involving an efficient solution algorithm, new magnetic field detection hardware and software designs. We show that this type of positioning has the benefits of not needing a line-of-sight between emitter and sensor, supporting a wide dynamic range, and can be applied to other medical systems in need of real-time positioning.

Person-Following Controller with Socially Acceptable Robot Motion

This work presents a novel stable controller for the person-following task that includes social considerations for a differential drive mobile robot equipped with an RGB-D camera and a laser range finder as main sensors. The proposed controller adapts its behavior based on the knowledge of both: a modified personal space distribution and human user velocity. Control objectives are focused hence on keeping the human user within the camera’s field-of-view while the mobile robot follows it, with a socially acceptable motion through arbitrary paths. To show the good behavior of this proposal, simulation and real experimental results are included and discussed. The asymptotic stability of the overall system is proved through the Lyapunov theory. Also, in our proposal, three state-of-the-art algorithms were integrated with the controller. In particular, a new real-time multi-person skeletal tracking system is used to obtain the relative human–robot position, a text to speech algorithm is used to confirm the commands given by the human, and also, a SLAM algorithm is used to obtain the map of the environment while the main task is being performed. Additionally, a hand gesture recognition module is included to interact with the mobile robot. This way, the robot is allowed to navigate with a socially-aware behavior in environments shared with humans. Finally, subjective and objective metrics are used as a validation method for human perception about the achieved robot motion.

WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions

Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.

Automated workflow analysis in vegetable grafting using an Ultra-Wide Band based real-time indoor location tracking system

• Automatically extracting information for workflow analysis from raw sensory data. • Developing a framework for workflow analysis using Ultra-Wide Band technology. • Identifying worker and task associations based on the location data. • Automatically calculating flow time, processing time, and waiting time from Gantt charts. • Achieving 90.54% accuracy in validating the proposed algorithms against the video data. Traditional workflow analysis requires intensive manual processes to extract meaningful information from raw sensory data. A novel framework for workflow analysis is developed based on a real-time indoor location tracking system utilizing Ultra-Wide Band technology. The proposed framework is applied to identify worker and task associations in a labor-intensive horticultural plant production process for grafted vegetable seedlings, based on location data. It involves binary segmentation of different distance measures calculated from location data to detect the plant tray’s movements and worker association in the vegetable grafting operation. An iterative overlap time removal algorithm is then implemented to ensure that no worker is assigned to overlapping processes, and no task is processed by multiple workers simultaneously. Key workflow metrices such as flow time, processing time, and waiting times are automatically calculated. Automatically generated Gantt charts and bar plots provide valuable insights into the workflow for management and help them identify improvement opportunities. Validation of results with video data show that the proposed workflow analysis is 90.5% accurate.

The olfactory gating of visual preferences to human skin and visible spectra in mosquitoes

Mosquitoes track odors, locate hosts, and find mates visually. The color of a food resource, such as a flower or warm-blooded host, can be dominated by long wavelengths of the visible light spectrum (green to red for humans) and is likely important for object recognition and localization. However, little is known about the hues that attract mosquitoes or how odor affects mosquito visual search behaviors. We use a real-time 3D tracking system and wind tunnel that allows careful control of the olfactory and visual environment to quantify the behavior of more than 1.3 million mosquito trajectories. We find that CO2 induces a strong attraction to specific spectral bands, including those that humans perceive as cyan, orange, and red. Sensitivity to orange and red correlates with mosquitoes’ strong attraction to the color spectrum of human skin, which is dominated by these wavelengths. The attraction is eliminated by filtering the orange and red bands from the skin color spectrum and by introducing mutations targeting specific long-wavelength opsins or CO2 detection. Collectively, our results show that odor is critical for mosquitoes’ wavelength preferences and that the mosquito visual system is a promising target for inhibiting their attraction to human hosts.

Development of real-time indoor human tracking system using LoRa technology

<p>Industrial growth has increased the number of jobs hence increase the number of employees. Therefore, it is impossible to track the location of all employees in the same building at the same time as they are placed in a different department. In this work, a real-time indoor human tracking system is developed to determine the location of employees in a real-time implementation. In this work, the long-range (LoRa) technology is used as the communication medium to establish the communication between the tracker and the gateway in the developed system due to its low power with high coverage range besides requires low cost for deployment. The received signal strength indicator (RSSI) based positioning method is used to measure the power level at the receiver which is the gateway to determine the location of the employees. Different scenarios have been considered to evaluate the performance of the developed system in terms of precision and reliability. This includes the size of the area, the number of obstacles in the considered area, and the height of the tracker and the gateway. A real-time testbed implementation has been conducted to evaluate the performance of the developed system and the results show that the system has high precision and are reliable for all considered scenarios.</p>

Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery.

.SignificanceDiffuse reflectance spectroscopy (DRS) allows discrimination of tissue type. Its application is limited by the inability to mark the scanned tissue and the lack of real-time measurements.AimThis study aimed to develop a real-time tracking system to enable localization of a DRS probe to aid the classification of tumor and non-tumor tissue.ApproachA green-colored marker attached to the DRS probe was detected using hue-saturation-value (HSV) segmentation. A live, augmented view of tracked optical biopsy sites was recorded in real time. Supervised classifiers were evaluated in terms of sensitivity, specificity, and overall accuracy. A developed software was used for data collection, processing, and statistical analysis.ResultsThe measured root mean square error (RMSE) of DRS probe tip tracking was and for the and dimensions, respectively. The diagnostic accuracy of the system to classify tumor and non-tumor tissue in real time was 94% for stomach and 96% for the esophagus.ConclusionsWe have successfully developed a real-time tracking and classification system for a DRS probe. When used on stomach and esophageal tissue for tumor detection, the accuracy derived demonstrates the strength and clinical value of the technique to aid margin assessment in cancer resection surgery.

Fiducial markers for stereotactic lung radiation therapy: review of the transthoracic, endovascular and endobronchial approaches.

Stereotactic body radiation therapy is an alternative to surgery for early-stage, inoperable peripheral non-small cell lung cancer. As opposed to linear accelerator (linac)-based (e.g. gating) and free-breathing techniques, CyberKnife® with Synchrony® technology allows accurate radiation delivery by means of a real-time respiratory motion tracking system using, in most cases, metal fiducial markers (FMs) placed in the vicinity of the target. The aims of this review are as follows. First, to describe the safety and efficacy of the transthoracic, endovascular and endobronchial FM insertion techniques for peripheral pulmonary lesions (PPLs). Second, to analyse performance in terms of the migration and tracking rates of different FM types. Recent developments in FM tracking for central lesions will also be reviewed. In conclusion, for PPLs, the endobronchial approach provides a low rate of pneumothorax, offers the possibility of concurrent diagnostic sampling for both the PPL and the lymph nodes, and, finally, reduces the intervention time compared to other techniques. In this context, coil-tailed and coil-spring FMs have shown the lowest migration rate with a consequently high tracking rate.

Real-time tracking of a moving target in an indoor corridor of the hospital building using RSSI signals received from two reference nodes.

In this paper, implementation and validation of a target tracking system based on the received signal strength indicator (RSSI) for an indoor corridor environment of the hospital is presented. Six tracking methods of a moving target (i.e., equipment, robot, or human) using RSSI signals measured from two stationary reference nodes located at the different sides of the corridor are proposed. A filter with its optimal weight value is also applied to smoothen and increase the accuracy of estimated position results (i.e., the x-position in the corridor). Additionally, a determination approach for finding the optimal parameters assigned for the proposed tracking methods and the filter are also introduced. The proposed methods are implemented in MATLAB/Simulink, and experiments using a 2.4 GHz, IEEE 802.15.4/ZigBee wireless network have been carried out in the indoor corridor of the hospital building. Experimental results obtained from the corridor size of 22 m demonstrate that our proposed methods can automatically and efficiently track the moving target in real time. The average distance errors, in the case of varying and manual tuning the optimal parameters of the proposed methods and the filter, reduce from 5.14 to 1.01 m and 4.55 to 0.86 m (i.e., two test cases; slow moving speed and double moving speed). Here, the errors decrease by 80.35% and 81.10%, respectively. For the case using the optimal parameters determined by the optimization approach, the average errors can reduce to 0.97 m for the first test case and 0.78 m for the second test case, respectively.Graphical abstractAn RSSI-based real-time tracking system for a moving target in an indoor corridor of the hospital building

Investigation on IoT based smart E-Inhaler integrated with mobile application

Asthma is a chronic disease in which patients are suffered from breathing difficulty due to anomalous functioning of the lungs. Worldwide about 350 million human beings were infected with the disease. As asthma has been rising predominantly, the disease should be monitored and effectively managed by self- monitoring. This aids both the clinical professionals and the patients so that it possesses control over the monitoring system and also helps in providing on-time medication effectively. In the advanced world, many existing portable devices are made available for users, but they have cost expensive and many feel inconvenient for using it. The proposed device for the Asthma infected persons jointly tracks the symptoms and location in real-time and gives an instant change in medication at the preferred time. The designed prototype overcomes the drawbacks of the existing model by connecting the patient to the physicians with Wi-Fi technology and by analysing the air quality around the patient’s environment. The proposed model consists of hardware units that manage the count of the dosage that has been actuated per medication and also determine the Air Quality Index of the patient’s surrounding parameters. In addition, the prototype comprises of software Android application, which offers the clinical assistants to have an eye on the real-time tracking system on the defined parameters. The medical assistants could analyse the data and the medical evaluation result has been able viewed by the patients on mobile phones. The developed features and the results show that the sensor and software aided mobile application to provide sufficient and real-time management of the disease of the affected.

A novel and intelligent decision-making system for real-time healthcare tracking using commercial wearable data

A novel and intelligent decision-making system for real-time healthcare tracking using commercial wearable data

A Dynamic HMM-Based Real-Time Location Tracking System Utilizing UHF Passive RFID

In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags’ received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags’ potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model’s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.

Determining Hybrid Re-id Features of Vehicles in Videos for Transport Analysis

The research topic presented in this paper belongs to computer vision problems in the transport application area, where the statistical data of the results give the input for the transport analysis. Although object tracking in a controlled environment could be performed with good results in general, accurate and detailed annotation of vehicles is a common problem in traffic analysis. Such annotation includes static and dynamic attributes of numerous vehicles. Most recent object trackers employ CNNs to compute the so-called re-identification features of the bounding boxes. In this paper we introduce hybrid re-identification features, which combine latent, static, and dynamic attributes to improve tracking. Furthermore, we propose a lightweight solution that could be integgrated in a real-time multi-camera tracking system.

Multi Face Real-Time Tracking System Based on DTN in Multi Camera Field of View

Multi Face Real-Time Tracking System Based on DTN in Multi Camera Field of View

Human identification and tracking using ultra-wideband-vision data fusion in unstructured environments

<p class="Abstract">Nowadays, the importance of working in changing and unstructured environments such as logistics warehouses through the cooperation between Automated Guided Vehicles (AGV) and the operator is increasingly demanded. The challenge addressed in this article aims to solve two crucial functions of autonomy: operator identification, and tracking. These tasks are necessary to enable an AGV to follow the selected operator along his path. This paper presents an innovative, accurate, robust, autonomous, and low-cost operator real-time tracking system, leveraging the inherent complementarity of the uncertainty regions (2D ellipses) between ultra-wideband (UWB) transceivers and cameras. The test campaign shows how the UWB system has higher uncertainty in the angular direction. In contrast, in the case of the vision system, the uncertainty is predominant along the radial coordinate. Due to the nature of the data, a sensor fusion demonstrates improvement in the accuracy and goodness of the final tracking.</p>

Exploring the Risky Travel Area and Behavior of Car-hailing Service

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

O-OGC03 Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancer specimens using diffuse reflectance spectroscopy

Abstract Background Diffuse reflectance spectroscopy (DRS) is a technique that allows discrimination of normal and abnormal tissue based on spectral data. It is a promising technique for cancer margin assessment. However, application in a clinical setting is limited by the inability of DRS to mark the tissue that has been scanned and its lack of continuous real-time spectral measurements. This aim of this study was to develop a real-time tracking system to enable localisation of the tip of a handheld DRS probe to aid classification of tumour and non-tumour tissue. Methods A coloured marker was attached to the DRS fibre probe and was detected using colour segmentation. A Kalman filter was used to estimate the probe’s tip position during scanning of the tissue specimen. In this way, the system was robust to partial occlusion allowing real-time detection and tracking. Supervised classification algorithms were used for the discrimination between tumour and non-tumour tissue, and evaluated in terms of overall accuracy, sensitivity, specificity, and the area under the curve (AUC). A live augmented view with all the tracked and classified optical biopsy sites were presented, providing visual feedback to the surgeons. Results A green coloured marker was successfully used to track the DRS probe. The measured root mean square error of probe tip tracking was 1.18±0.58mm and 1.05±0.28mm for the X and Y directions, respectively, whilst the maximum measured error was 1.76mm. Overall, 47 distinct sets of tumour and non-tumour tissue data were recorded through real-time tracking of ex vivo oesophageal and gastric tissue. The overall diagnostic accuracy of the system to classify tumour and non-tumour tissue in real-time was 94% for stomach and 96% for the oesophagus. Conclusions We have been able to successfully develop a real-time tracking system for a DRS probe when used on stomach and oesophageal tissue for tumour detection, and the accuracy derived demonstrates the strength and clinical value of the technique. The method allows real-time tracking and classification with short data acquisition time to aid margin assessment in cancer resection surgery.

Experimental Validation of Real-Time Ski Jumping Tracking System Based on Wearable Sensors.

For sports scientists and coaches, its crucial to have reliable tracking systems to improve athletes. Therefore, this study aimed to examine the validity of a wearable real-time tracking system (WRRTS) for the quantification of ski jumping. The tracking system consists of wearable trackers attached to the ski bindings of the athletes and fixed antennas next to the jumping hill. To determine the accuracy and precision of the WRRTS, four athletes of the German A or B National Team performed 35 measured ski jumps. The WRRTS was used to measure the 3D positions and ski angles during the jump. The measurements are compared with camera measurements for the in-flight parameters and the official video distance for the jumping distance to assess their accuracy. We statistically evaluated the different methods using Bland–Altman plots. We thereby find a mean absolute error of for the jumping distance, for the in-flight positions, and °, and ° for the camera projected pitch and V-style opening angle, respectively. We show the validity of the presented WRRTS to measure the investigated parameters. Thus, the system can be used as a tracking system during training and competitions for coaches and sports scientists. The real-time feature of the tracking system enables usage during live TV broadcasting.

Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme.

The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications.