Development of laboratory devices for real-time measurement of object 3D position using stereo cameras

2021 ◽  
Vol 57 (2) ◽  
pp. 025006
Author(s):  
Sigit Ristanto ◽  
Waskito Nugroho ◽  
Eko Sulistya ◽  
Gede B Suparta
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Physical Phenomenon ◽  
Stereo Image ◽  
Position Measurement ◽  
Average Delay ◽  
Object Position ◽  
Stereo Camera ◽  
3D Object ◽  
Object Distance

Abstract Measuring the 3D position at any time of a given object in real-time automatically and well documented to understand a physical phenomenon is essential. Exploring a stereo camera in developing 3D images is very intriguing since a 3D image perception generated by a stereo image may be reprojected back to generate a 3D object position at a specific time. This research aimed to develop a device and measure the 3D object position in real-time using a stereo camera. The device was constructed from a stereo camera, tripod, and a mini-PC. Calibration was carried out for position measurement in X, Y, and Z directions based on the disparity in the two images. Then, a simple 3D position measurement was carried out based on the calibration results. Also, whether the measurement was in real-time was justified. By applying template matching and triangulation algorithms on those two images, the object position in the 3D coordinate was calculated and recorded automatically. The disparity resolution characteristic of the stereo camera was obtained varied from 132 pixels to 58 pixels for an object distance to the camera from 30 cm to 70 cm. This setup could measure the 3D object position in real-time with an average delay time of less than 50 ms, using a notebook and a mini-PC. The 3D position measurement can be performed in real-time along with automatic documentation. Upon the stereo camera specifications used in this experiment, the maximum accuracy of the measurement in X, Y, and Z directions are ΔX = 0.6 cm, ΔY = 0.2 cm, and ΔZ = 0.8 cm at the measurement range of 30 cm–60 cm. This research is expected to provide new insights in the development of laboratory tools for learning physics, especially mechanics in schools and colleges.

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

A real-time TV logo tracking method using template matching

2012 ◽  
Author(s):  
Zhi Li ◽  
Xinzhu Sang ◽  
Binbin Yan ◽  
Junmin Leng
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Tracking Method

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

scholarly journals Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals

2022 ◽  
Vol 12 ◽  
Author(s):  
Silvia Seoni ◽  
Simeon Beeckman ◽  
Yanlu Li ◽  
Soren Aasmul ◽  
Umberto Morbiducci ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Assessment ◽  
Real Time ◽  
Template Matching ◽  
Quality Metrics ◽  
Laser Doppler ◽  
Signal Quality ◽  
Laser Doppler Vibrometry ◽  
Quality Metric

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

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