Camera Tracking System Research Articles

Two-screw osteosynthesis is biomechanically superior to single-screw osteosynthesis for type II odontoid fractures

The data on the use of a one- or two-screw technique (1S, 2S) for ventral osteosynthesis of type II dens fractures are contradictory. The aim was to design an apparatus to mimic the physiological conditions and test stability with 1S, 2S, and a headless compression screw (HCS) for osteosynthesis of artificially created type II odontoid fractures. The apparatus was mounted on a Zwick materials testing machine. A total of 18 C1–2 specimens were stratified into three groups (1S, 2S, HCS). Odontoid fractures were artificially created, and osteosynthesis was performed. Each specimen was tested at loads increasing from 1 to 40 N. Screw loosening was observed visually, by fatigue data, and by a camera tracking system. Analysis of the Zwick data and the camera data revealed a significant higher stability after 2S compared to 1S and HCS treatment (Zwick data: p = 0.021, camera data: p < 0.001), while visible screw loosening showed a superiority of the 2S only over HCS (p = 0.038). The developed apparatus allowed the dynamic study of the atlantoaxial joint with a high approximation to physiological conditions. The results demonstrated superiority of the 2S over the 1S and HCS in biomechanical stability in the treatment of type II odontoid fractures.

Comparison of experimental measurements and fast Monte Carlo simulations for typical set-ups in fluoroscopically-guided interventional procedures

PyMCGPU-IR is an application based on the Monte Carlo code MCGPU-IR that automatically retrieves procedure information from X-ray systems and medical worker positions from a tracking camera system. PyMCGPU-IR calculates personal dose equivalent values, doses in organs and effective dose values for both patient and medical staff in interventional radiology procedures and displays them visually. The code's main advantage lies in its time efficiency, enabling simulations in under 2 min with statistical uncertainties below 5% (k = 2).This study involved testing in a hospital room using an interventional X-ray system. A RANDO phantom simulated the patient, with passive dosimeters affixed to the back for measuring skin dose values. A PMMA slab phantom represented the operator, with passive and active dosimeters affixed to its front surface to measure Hp(10). The irradiation conditions were simulated with PyMCGPU-IR using voxelized geometries to represent both phantoms. Results demonstrate good agreement between PyMCGPU-IR simulations and measured patient skin dose values, with differences of up to 5% for mean skin dose and up to 14% for the peak skin dose. Concerning Hp(10) values on the operator phantom, PyMCGPU-IR calculated values fall within the uncertainty ranges of dosimeter measurements for most points. The highest Hp(10) discrepancy is 42%, which is acceptable when compared with the typical variability observed between active and passive personal dosimeters measurements in interventional radiology. The results demonstrate PyMCGPU-IR's satisfactory performance for patient and personal dosimetry, compared to existing solutions like commercial skin dose calculation software and personal physical dosimeters.

An augmented reality and high-speed optical tracking system for laparoscopic surgery.

While minimally invasive laparoscopic surgery can help reduce blood loss, reduce hospital time, and shorten recovery time compared to open surgery, it has the disadvantages of limited field of view and difficulty in locating subsurface targets. Our proposed solution applies an augmented reality (AR) system to overlay pre-operative images, such as those from magnetic resonance imaging (MRI), onto the target organ in the user's real-world environment. Our system can provide critical information regarding the location of subsurface lesions to guide surgical procedures in real time. An infrared motion tracking camera system was employed to obtain real-time position data of the patient and surgical instruments. To perform hologram registration, fiducial markers were used to track and map virtual coordinates to the real world. In this study, phantom models of each organ were constructed to test the reliability and accuracy of the AR-guided laparoscopic system. Root mean square error (RMSE) was used to evaluate the targeting accuracy of the laparoscopic interventional procedure. Our results demonstrated a registration error of 2.42 ± 0.79 mm and a procedural targeting error of 4.17 ± 1.63 mm using our AR-guided laparoscopic system that will be further refined for potential clinical procedures.

Match running performance is influenced by possession and team formation in an English Premier League team.

The aim of this study was to examine the possession (very low, low, high, and very high), team formation (3-5-2 and 4-3-3) and position (centre-backs, full-backs, centre midfielders, attacking midfielders, and centre forwards) on match load across two consecutive seasons in elite soccer. Twenty-seven English Premier League outfield players were recruited. Data was monitored through an 18 Hz Global Positioning System and a 25 Hz semi-automated camera tracking system, respectively, and all variables were analysed per minute. Main effects for formation on total distance (TD) (p = 0.006; η 2 = 0.010), high-speed running (HSR) (p = 0.009; η 2 = 0.009), number of high metabolic load (HML) efforts (p = 0.004; η 2 = 0.011) were observed. In addition, there were significant interaction effects with formation × possession on TD (p < 0.001; η 2 = 0.043), HSR (p = 0.006; η 2 = 0.018), sprinting (p < 0.001; η 2 = 0.030), HML efforts (p < 0.001; η 2 = 0.035), accelerations (p < 0.001; η 2 = 0.025). From the position-specific analysis, only the running performance of centre-backs was affected by formation or positional factors. These results indicate that formation and possession can have a significant impact on TD, HSR, and HML distance. Furthermore, players performed more high-intensity efforts in 3-5-2 than 4-3-3 formation. These findings suggest that coaches can evaluate running performance in the context of formation and possession and tailor tactical strategies to optimise physical performance.

Body Motion Auto Tracking Camera System for Online Class Education Supporting Device using OpenCV and Microcontroller

In the online teaching process, a teacher is necessary to describe the material clearly and have a broad perspective so that the material can be delivered completely. The problem occurs because the camera has different viewing angles, which results in limited movement of people who are teachers whenever explain material on a broad chalkboard. For this reason, a motion tracking camera is designed like a common camera that can move to follow the teacher's upper body. The camera will be connected to servo motors such as MG996R to move the camera on the X-Axis and Y-Axis. Then with OpenCV technology, the camera will track the teacher as an object and follow the direction of his/her movement. The results from thisresearch, it is concluded that by integrating Python into a video conference application and without it, the system can detect the upper body at 2-6 meters. For the X-Axis servo motor angle for conditions using video conference and without video conference application, the servo can rotate by detecting the upper body at angles of 30deg, 60deg, 90deg, 120deg, and 150deg. Then on the Y-Axis movement, it can rotate by detecting the upper body at 90deg, 100deg, 110deg, 120deg and 130deg angles. The test was carried out in a room with an area of 9.1 m x 7.4 m, the light condition of the room was 116 lux the object height was 1.5 m, and the camera height was 0.8 m above the floor

Conducting an Analysis of Mosquito Flight Behaviors in a Wind Tunnel.

Prior to conducting wind tunnel experiments, mosquitoes must be prepared for testing. Important factors and state-dependent processes of the mosquito-like the sex, age, infection status, reproductive status, or nutritional status-should be evaluated and motivated by questions and hypotheses one seeks to address. Other critical external factors that can impact the mosquitoes' behavior and should be controlled for both in the colony and in the room where the wind tunnel experiments take place include the circadian rhythm, room temperature, light intensity, and relative humidity. Together, the internal and external factors, and wind tunnel design, ultimately control the behavior of the mosquito and, hence, the success of the experiments. In the present protocol, we describe methods using a standard wind tunnel design in which the fan pulls the air through the working section of the wind tunnel and the mosquito behavior is recorded by a multicamera system. Variations around the camera tracking system can be adapted according to the research questions being asked and include real-time tracking for both closed-loop and open-loop control of the stimulus environment or recording of the videos for off-line digitization and analysis. Within the working section, the sensory environment (odor, visual, wind) can be controlled to test the mosquito responses to different stimuli, and below we include different equipment and tools for modifying the stimuli the mosquito experiences during flight. Finally, the methods described here are applicable to multiple mosquito species, although the experiment parameters may need to be changed (e.g., ambient luminosity).

Experimental Study on Motion Law of the Fragment at Hypersonic Speed

As a damage element, high-speed fragments have a significant effect on the ammunition safety. The impact from the fragments are also one of the basic problems of ammunition safety tests. To clarify the reaction characteristics of combustion, explosion, detonation, and so on, when hypersonic fragments hit insensitive munitions, it is necessary to carry out corresponding research on the deceleration law of hypersonic fragment in the air. In this paper, a 30 mm caliber gun with large chamber, small caliber, and large aspect ratio is proposed to drive high-speed fragments. According to STANAG 4496 standard, a near-cylinder steel fragment with Brinell hardness HB ≤ 270 and mass of 18.6 g was designed. The test system was composed of zone interception velocity measurement, chamber pressure sensor, trajectory tracking system, high-speed camera, and other equipment were also established to obtain the pressure variations in the chamber, the velocity of the fragment, and its flight orientation. From the video taken by the high-speed camera and trajectory tracking system, the fragment and the projectile sabot achieve effective separation after the fragment travels out of the muzzle. As time goes on, the distance between the fragment and the projectile sabot gradually increases. The fragment is always in the front of the sabot and steadily flies to the target. The muzzle velocity of the fragment is controlled by adjusting the propellant charge, and the flight velocity in the air is measured by the zone interception velocity measuring device in the range of 5 Ma to 7 Ma. The theoretical models of fragment deceleration and the models of flight orientation are also established according to the experimental data. On this basis, F test and least square nonlinear regression fitting were used to analyze experimental data. Finally, the deceleration coefficient of quasi-cylindrical fragments between 5 Ma and 7 Ma stipulated in STANAG 4496 standard is 0.009312, and the average drag coefficient in air is 1.109.

Algorithm Design of Mirror Tracking System

In the current national defense background, the requirements for the performance of weapons are increasing day by day, the speed of the flying target and the flight state provide important parameters for the improvement of the characteristics of weapons, ballistics and ammunition. In the shooting range test, the high-speed flying target is tracked and photographed to obtain important indicators such as the flight status of the flying target in high-speed flight. This paper studies a ballistic tracking camera system based on a rotating mirror. It is composed of a system, which can track the flight trajectory of the flying target in the air in real time, and obtain important information of the flying target in flight through high-speed camera shooting. Design the mirror tracking system to capture the flight status of the flying target, analyze the flight trajectory parameters of the target through MATLAB, and optimize the accuracy and response speed of the motor control algorithm. The motor turns the mirror to reflect the flying target to the high-speed camera. The feasibility of the system is ver ifi ed. Th e tot al sc ann ing range of the system for tracking flying targets is 90°, and the scanning accuracy is ±0.5°.

Development of an Automatic Tracking Camera System Integrating Image Processing and Machine Learning

This paper describes the development of a robust object tracking system that combines detection methods based on image processing and machine learning for automatic construction machine tracking cameras at unmanned construction sites. In recent years, unmanned construction technology has been developed to prevent secondary disasters from harming workers in hazardous areas. There are surveillance cameras on disaster sites that monitor the environment and movements of construction machines. By watching footage from the surveillance cameras, machine operators can control the construction machines from a safe remote site. However, to control surveillance cameras to follow the target machines, camera operators are also required to work next to machine operators. To improve efficiency, an automatic tracking camera system for construction machines is required. We propose a robust and scalable object tracking system and robust object detection algorithm, and present an accurate and robust tracking system for construction machines by integrating these two methods. Our proposed image-processing algorithm is able to continue tracking for a longer period than previous methods, and the proposed object detection method using machine learning detects machines robustly by focusing on their component parts of the target objects. Evaluations in real-world field scenarios demonstrate that our methods are more accurate and robust than existing off-the-shelf object tracking algorithms while maintaining practical real-time processing performance.

21.2: The Design and Optimization of Dual‐Camera based Eye Tracking System for Dynamic Autostereoscopic 3D Display

Eye‐tracking based autostereoscopic 3D display is a promising technology to provide high resolution and low crosstalk stereoscopic images to the user without wearing any glasses. However, current systems fail to provide satisfactory viewing experience due to inherent latency caused by the tracking camera system and algorithm computation time and the low reliability of tracking algorithms, especially when users are in fast motion. High instantaneous crosstalk and intermittent interference patterns will be observed, which significantly degrades the immersive level of the viewing experience. This becomes even worse when the autostereoscopic 3D display is used to provide a simulated reality (SR) experience, in which the user constantly moves the head to explore the virtual reality space from different viewing directions. To solve the problem, this paper proposes a dual‐camera tracking system, focusing on the key design factors to improve its tracking reliability and reduce the user perceived latency. For the latter, it leverages motion prediction and compensate the latency with predictive rendering technique. To measure the performance, this paper defines the Perceived Autostereoscopic 3D Display Motion to Picture (PAMTP) latency to represent the impact of the system latency on the viewing experience. PAMTP latency and the instantaneous crosstalk are evaluated via simulations and experiments, which show our method can reduce the instantaneous crosstalk by nearly 70% and provide highly reliable tracking.

Modeling and Reconstruction of State Variables for Low-Level Control of Soft Pneumatic Actuators.

To further advance closed-loop control for soft robotics, suitable sensor and modeling strategies have to be investigated. Although there are many flexible and soft sensors available, the integration into the actuator and the use in a control loop is still challenging. Therefore, a state-space model for closed-loop low-level control of a fiber-reinforced actuator using pressure and orientation measurement is investigated. To do so, the integration of an inertial measurement unit and geometric modeling of actuator is presented. The piecewise constant curvature approach is used to describe the actuator’s shape and deformation variables. For low-level control, the chamber’s lengths are reconstructed from bending angles with a geometrical model and the identified material characteristics. For parameter identification and model validation, data from a camera tracking system is analyzed. Then, a closed-loop control of pressure and chambers’ length of the actuator is investigated. It will be shown, that the reconstruction model is suitable for estimating the state variables of the actuator. In addition, the use of the inertial measurement unit will demonstrate a cost-effective and compact sensor for soft pneumatic actuators.

Study on Design and Implementation of Distributed Multiple Camera Surveillance and Tracking System

This work proposes a system to successfully track, identify and tag target objects or individuals in a real time environment. Consider a sequence of cameras installed in an environment or a facility. Through these cameras user can track those intruders who are aware of how the surveillance system operates and are actively trying to avoid getting seen by the surveillance system. This system tracks the movement of any person’s movement and record and maintain that data throughout any facility in which such a solution is deployed. Movement logging of any individual person can also be done if it does not infringe his/her privacy. Design and analysis a distributed algorithm for the optimization of the recognition and mapping of the given subject/subjects on a User Interface. Finally, the performance and robustness of the distributed system is further analyzed through continuously training the algorithm or maybe real time demo.

KeySLAM: Robust RGB-D Camera Tracking Using Adaptive VO and Optimal Key-Frame Selection

We propose a novel RGB-D camera tracking system that robustly reconstructs hand-held RGB-D camera sequences. The robustness of our system is achieved by two independent features of our method: adaptive visual odometry (VO) and integer programming-based key-frame selection. Our VO method adaptively interpolates the camera motion results of the direct VO (DVO) and the iterative closed point (ICP) to yield more optimal results than existing methods such as Elastic-Fusion. Moreover, our key-frame selection method locates globally optimum key-frames using a comprehensive objective function in a deterministic manner rather than heuristic or experience-based rules that prior methods mostly rely on. As a result, our method can complete reconstruction even if the camera fails to be tracked due to discontinuous camera motions, such as kidnap events, when conventional systems need to backtrack the scene.

Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking.

In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.

DM-SLAM: Monocular SLAM in Dynamic Environments

Many classic visual monocular SLAM (simultaneous localization and mapping) systems have been developed over the past decades, yet most of them fail when dynamic scenarios dominate. DM-SLAM is proposed for handling dynamic objects in environments based on ORB-SLAM2. This article mainly concentrates on two aspects. Firstly, we proposed a distribution and local-based RANSAC (Random Sample Consensus) algorithm (DLRSAC) to extract static features from the dynamic scene based on awareness of the nature difference between motion and static, which is integrated into initialization of DM-SLAM. Secondly, we designed a candidate map points selection mechanism based on neighborhood mutual exclusion to balance the accuracy of tracking camera pose and system robustness in motion scenes. Finally, we conducted experiments in the public dataset and compared DM-SLAM with ORB-SLAM2. The experiments corroborated the superiority of the DM-SLAM.

EMG-driven hand model based on the classification of individual finger movements

The recovery of hand motion is one of the most challenging aspects in stroke rehabilitation. This paper presents an initial approach to robot-assisted hand-motion therapies. Our goal was twofold: firstly, we have applied machine learning methods to identify and characterize finger motion patterns from healthy individuals. To this purpose, Electromyographic (EMG) signals have been acquired from flexor and extensor muscles in the forearm using surface electrodes. Time and frequency features were used as inputs to machine learning algorithms for recognition of six hand gestures. In particular, we compared the performance of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and k-Nearest Neighbor (k-NN) algorithms for classification. Secondly, each identified gesture was turned into a joint reference trajectory by applying interpolation methods. This allowed us to reconstruct the hand/finger motion kinematics and to simulate the dynamics of each motion pattern. Experiments were carried out to create an EMG database from 20 control subjects, and a VICON camera tracking system was used to validate the accuracy of the proposed system. The average correlation between the EMG-based generated joint trajectories and the tracked hand-motion was 0.91. Furthermore, statistical analysis applied to 14 different SVM, ANN and k-NN configurations showed that Fine k-NN and Weighted k-NN have a better performance for the classification of gestures (98% of accuracy). In a future, the trajectories controlled by EMG signals could be applied to an exoskeleton or hand-robotic prosthesis for rehabilitation.

Acoustic Enhanced Camera Tracking System Based on Small-Aperture MEMS Microphone Array

The camera tracking systems based on visual image processing face a problem that they are completely ineffective in their blind zones. To address this problem, a design of acoustic enhanced tracking system combining visual and auditory target tracking methods is reported in this article. The system holds the abilities of performing sound direction estimation and target tracking in real-time. Estimating direction of arrival of the sound accompanied with the target helps the camera turn towards the target outside the field of view. This sound-triggered mode of camera operation makes a significant supplement to conventional cameras' working state. Considering the embedded system is necessary in consideration of the cost and size of the system in practical application, we designed a small aperture array with 7 digital omnidirectional MEMS microphones and built the overall system based on FPGA and ARM. The experiments were carried out in a normal indoor environment and the results confirmed that the system can perform auditory and visual tracking in real-time.

Syringe infusion pump with absolute piston displacement control.

A vast majority of syringe pumps operate on stepper motors, which limits their effectiveness for precision fluid delivery using estimation algorithms. Such a system also hampers the ability to ascertain if the infusion or aspiration instruction has been correctly carried out in the event of power interruptions. To address this issue, a linear servo based actuator system is described to provide absolute indications of the plunger position. System performance in terms of linearity and reliability of plunger translation were verified using a camera tracking system with syringe capacities ranging from 3 to 50 ml and at syringe plunger speeds ranging from 1 to 6.6 mm/s when distilled water was used as the medium. In investigations involving more viscous liquids, the system revealed similarly linear characteristics with 50% glycerol-water (v/v), but cyclical stick-slip behavior with Freund's adjuvant.

A Camera Tracking System Based on Closed-loop Kernelized Correlation Filters

Camera tracking is an important application in the computer vision. With the progress of the tracking-by-detection algorithm, industrial cameras can track targets autonomously. However, during the long-time tracking, it’s prone to miss target owing to heavy object occlusion, environment changing and objects appearance variations. Our camera tracking system is based on the kernelized correlation filter to track object, and we add a self-verification module to judge if the current tracking results are reliable. This can be useful in solving target missing when object meets occlusion or variations, and avoid model drift during long-time detection. Last but not the least, we keep the targets in the corner of the image by controlling our camera, avoiding object’s moving out of view. The kernel correlation filter bounded with self-verification and re-detection modules boost the performance. Extensive experiments on the OTB-2013 benchmark show that it performs better than state-of-the-art methods.

Offside Detection System Using an Infrared Camera Tracking System

This paper describes an experimental offside detection system that will be capable of detecting offside passes during a game of soccer. Soccer is the world’s most popular and most televised sport. In recent years, FIFA has implemented goal line technology in order to end controversial goals/missed goals during high profile competitive matches. The most contentious aspect of the sport is the offside rule and its many controversial calls or lack of calls. Sometimes the linesmen cannot see the passage of playing fast enough to make a correct decision. Being similar to goal line technology, people have requested offside technology to help the linesmen and to reduce the number of incorrect offside calls in a game. This paper describes a working offside detection system that can accurately detect offside passes. Positional data was exported from a VICON infrared motion tracking camera system and a MATLAB script was written so that it can analyze the positions of the players and the ball and determine if a pass was offside.