Reconstruction of isolated moving objects with high 3D frame rate based on phase shifting profilometry

2019 ◽  
Vol 438 ◽  
pp. 61-66 ◽  
Author(s):  
Lei Lu ◽  
Zhaoyi Jia ◽  
Yinsen Luan ◽  
Jiangtao Xi
Keyword(s):  
Moving Objects ◽  
Phase Shifting ◽  
Frame Rate

Blink-Spot Projection Method for Fast Three-Dimensional Shape Measurement

2015 ◽  
Vol 27 (4) ◽  
pp. 430-443 ◽  
Author(s):  
Jun Chen ◽  
◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
Takeshi Takaki ◽  
...  
Keyword(s):  
Projection Method ◽  
Fast Moving ◽  
High Speed ◽  
Degrees Of Freedom ◽  
Moving Objects ◽  
Three Dimensional ◽  
Frame Rate ◽  
Light Illumination ◽  
Camera System ◽  
3D Shape

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/13.jpg"" width=""300"" /> Blink-spot projection method</div> We present a blink-spot projection method for observing moving three-dimensional (3D) scenes. The proposed method can reduce the synchronization errors of the sequential structured light illumination, which are caused by multiple light patterns projected with different timings when fast-moving objects are observed. In our method, a series of spot array patterns, whose spot sizes change at different timings corresponding to their identification (ID) number, is projected onto scenes to be measured by a high-speed projector. Based on simultaneous and robust frame-to-frame tracking of the projected spots using their ID numbers, the 3D shape of the measuring scene can be obtained without misalignments, even when there are fast movements in the camera view. We implemented our method with a high-frame-rate projector-camera system that can process 512 × 512 pixel images in real-time at 500 fps to track and recognize 16 × 16 spots in the images. Its effectiveness was demonstrated through several 3D shape measurements when the 3D module was mounted on a fast-moving six-degrees-of-freedom manipulator. </span>

scholarly journals Reconstruction of Isolated Moving Objects by Motion-Induced Phase Shift Based on PSP

2021 ◽  
Vol 12 (1) ◽  
pp. 252
Author(s):  
Ke Wu ◽  
Min Li ◽  
Lei Lu ◽  
Jiangtao Xi
Keyword(s):  
Phase Shift ◽  
Fringe Pattern ◽  
Moving Objects ◽  
Object Motion ◽  
Moving Object ◽  
Phase Shifting ◽  
Phase Information ◽  
Object Reconstruction ◽  
Fringe Patterns

The reconstruction of moving objects based on phase shifting profilometry has attracted intensive interests. Most of the methods introduce the phase shift by projecting multiple fringe patterns, which is undesirable in moving object reconstruction as the errors caused by the motion will be intensified when the number of the fringe pattern is increased. This paper proposes the reconstruction of the isolated moving object by projecting two fringe patterns with different frequencies. The phase shift required by the phase shifting profilometry is generated by the object motion, and the model describing the motion-induced phase shift is presented. Then, the phase information in different frequencies is retrieved by analyzing the influence introduced by movement. Finally, the mismatch on the phase information between the two frequencies is compensated and the isolated moving object is reconstructed. Experiments are presented to verify the effectiveness of the proposed method.

High-Frame-Rate Structured Light 3-D Vision for Fast Moving Objects

2014 ◽  
Vol 26 (3) ◽  
pp. 311-320 ◽  
Author(s):  
Yongjiu Liu ◽  
◽  
Hao Gao ◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
...  
Keyword(s):  
Fast Moving ◽  
Moving Objects ◽  
Vision System ◽  
Structured Light ◽  
Gray Code ◽  
Frame Rate ◽  
3D Vision ◽  
High Frame Rate ◽  
Depth Images ◽  
Structured Light Vision

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260003/04.jpg"" width=""300"" />HFR 3D vision system</span></div> This paper presents a fast motion-compensated structured-light vision system that realizes 3-D shape measurement at 500 fps using a high-frame-rate camera-projector system. Multiple light patterns with an 8-bit gray code, are projected on the measured scene at 1000 fps, and are processed in real time for generating 512 × 512 depth images at 500 fps by using the parallel processing of a motion-compensated structured-light method on a GPU board. Several experiments were performed on fast-moving 3-D objects using the proposed method. </span>

Laser diode phase-shifting interferometer operating at a frame rate

1999 ◽  
Author(s):  
Ribun Onodera ◽  
Norio Onda ◽  
Yukihiro Ishii
Keyword(s):  
Laser Diode ◽  
Phase Shifting ◽  
Frame Rate

scholarly journals Probabilistic Evaluation and Filtering of Image Velocimetry Measurements

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2206
Author(s):  
Evangelos Rozos ◽  
Katerina Mazi ◽  
Antonis D. Koussis
Keyword(s):  
Camera Calibration ◽  
Moving Objects ◽  
Probabilistic Approach ◽  
Video Recording ◽  
Calibration Procedure ◽  
Frame Rate ◽  
Acceptable Error ◽  
Practical Applications ◽  
Image Velocimetry ◽  
The Impact

The recent technological advances in remote sensing (e.g., unmanned aerial vehicles, digital image acquisition, etc.) have vastly improved the applicability of image velocimetry in hydrological studies. Thus, image velocimetry has become an established technique with an acceptable error for practical applications (the error can be lower than 10%). The main source of errors has been attributed to incomplete intrinsic and extrinsic camera calibration, to non-constant frame rate and to spurious low velocities due to moving objects that are irrelevant to the streamflow. Some researchers have even employed probabilistic approaches (Monte Carlo simulations) to analyze the uncertainty introduced during the camera calibration procedure. On the other hand, the endogenous uncertainty of the image velocimetry algorithms per se has received little attention. In this study, a probabilistic approach is employed to systematically analyze this uncertainty. It is argued that this analysis may not only improve the performance of the image velocimetry methods but it can also provide information regarding the impact of the video recording conditions (e.g., low density of features, oblique camera angle, low resolution, etc.) on the accuracy of the estimated values. The suggested method has been tested in six case studies of which the data have been previously made publicly available by independent researchers.

scholarly journals Asynchronous Semantic Background Subtraction

2020 ◽  
Vol 6 (6) ◽  
pp. 50
Author(s):  
Anthony Cioppa ◽  
Marc Braham ◽  
Marc Van Droogenbroeck
Keyword(s):  
Real Time ◽  
Background Subtraction ◽  
Semantic Information ◽  
Moving Objects ◽  
Temporal Evolution ◽  
Semantic Segmentation ◽  
Feedback Mechanism ◽  
Segmentation Algorithm ◽  
Frame Rate ◽  
The One

The method of Semantic Background Subtraction (SBS), which combines semantic segmentation and background subtraction, has recently emerged for the task of segmenting moving objects in video sequences. While SBS has been shown to improve background subtraction, a major difficulty is that it combines two streams generated at different frame rates. This results in SBS operating at the slowest frame rate of the two streams, usually being the one of the semantic segmentation algorithm. We present a method, referred to as “Asynchronous Semantic Background Subtraction” (ASBS), able to combine a semantic segmentation algorithm with any background subtraction algorithm asynchronously. It achieves performances close to that of SBS while operating at the fastest possible frame rate, being the one of the background subtraction algorithm. Our method consists in analyzing the temporal evolution of pixel features to possibly replicate the decisions previously enforced by semantics when no semantic information is computed. We showcase ASBS with several background subtraction algorithms and also add a feedback mechanism that feeds the background model of the background subtraction algorithm to upgrade its updating strategy and, consequently, enhance the decision. Experiments show that we systematically improve the performance, even when the semantic stream has a much slower frame rate than the frame rate of the background subtraction algorithm. In addition, we establish that, with the help of ASBS, a real-time background subtraction algorithm, such as ViBe, stays real time and competes with some of the best non-real-time unsupervised background subtraction algorithms such as SuBSENSE.

scholarly journals Fast Object Detection Using Dimensional Based Features for Public Street Environments

Smart Cities ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 93-111 ◽  
Author(s):  
Ivan Matveev ◽  
Kirill Karpov ◽  
Ingo Chmielewski ◽  
Eduard Siemens ◽  
Aleksey Yurchenko
Keyword(s):  
Low Power ◽  
Near Infrared ◽  
Moving Objects ◽  
Infrared Camera ◽  
Machine Learning Algorithms ◽  
Frame Rate ◽  
Raspberry Pi ◽  
Real World Data ◽  
Iot Devices

Modern object recognition algorithms have very high precision. At the same time, they require high computational power. Thus, widely used low-power IoT devices, which gather a substantial amount of data, cannot directly apply the corresponding machine learning algorithms to process it due to the lack of local computational resources. A method for fast detection and classification of moving objects for low-power single-board computers is shown in this paper. The developed algorithm uses geometric parameters of an object as well as scene-related parameters as features for classification. The extraction and classification of these features is a relatively simple process which can be executed by low-power IoT devices. The algorithm aims to recognize the most common objects in the street environment, e.g., pedestrians, cyclists, and cars. The algorithm can be applied in the dark environment by processing images from a near-infrared camera. The method has been tested on both synthetic virtual scenes and real-world data. The research showed that a low-performance computing system, such as a Raspberry Pi 3, is able to classify objects with acceptable frame rate and accuracy.

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