scholarly journals Real-time motion tracking using optical flow on multiple GPUs

2014 ◽  
Vol 62 (1) ◽  
pp. 139-150 ◽  
Author(s):  
S.A. Mahmoudi ◽  
M. Kierzynka ◽  
P. Manneback ◽  
K. Kurowski
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Motion Tracking ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
High Definition ◽  
Real Time Processing ◽  
Multiple Gpus ◽  
Time Motion ◽  
Harris Corner

Abstract Motion tracking algorithms are widely used in computer vision related research. However, the new video standards, especially those in high resolutions, cause that current implementations, even running on modern hardware, no longer meet the needs of real-time processing. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have recently been proposed. Although they present a great potential of a GPU platform, hardly any is able to process high definition video sequences efficiently. Thus, a need arose to develop a tool being able to address the outlined problem. In this paper we present software that implements optical flow motion tracking using the Lucas-Kanade algorithm. It is also integrated with the Harris corner detector and therefore the algorithm may perform sparse tracking, i.e. tracking of the meaningful pixels only. This allows to substantially lower the computational burden of the method. Moreover, both parts of the algorithm, i.e. corner selection and tracking, are implemented on GPU and, as a result, the software is immensely fast, allowing for real-time motion tracking on videos in Full HD or even 4K format. In order to deliver the highest performance, it also supports multiple GPU systems, where it scales up very well

scholarly journals Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening

2021 ◽  
Vol 13 (18) ◽  
pp. 3674
Author(s):  
Guangqi Xie ◽  
Mi Wang ◽  
Zhiqi Zhang ◽  
Shao Xiang ◽  
Luxiao He
Keyword(s):  
Real Time ◽  
Graphics Processing Unit ◽  
Geometric Distortion ◽  
Processing Unit ◽  
Registration Method ◽  
Real Time Processing ◽  
Time Processing ◽  
Central Processing ◽  
Registration Errors ◽  
Processing Accuracy

This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.

scholarly journals New Parallel Technics for GPU, Fast SURF Algorithm

2020 ◽  
Vol 9 (8) ◽  
pp. 389-393
Keyword(s):  
Real Time ◽  
Graphics Processing Unit ◽  
Computation Time ◽  
Processing Unit ◽  
Real Time Processing ◽  
Time Usage ◽  
Computation Algorithm ◽  
Original Application ◽  
Graphics Processing ◽  
Point Detection

Computer vision algorithms, especially real-time tasks, require intensive computation and reduced time. That’s why many algorithms are developed for interest point detection and description. For instance, SURF (Speeded Up Robust Feature) is extensively adopted in tracking or detecting forms and objects. SURF algorithm remains complex and massive in term of computation. So, it’s a challenge for real time usage on CPU. In this paper we propose a fast SURF parallel computation algorithm designed for Graphics-Processing-Unit (GPU). We describe different states of the algorithm in detail, using several optimizations. Our method can improve significantly the original application by reducing the computation time. Thus, it presents a good performance for real-time processing

The Realization of Real-Time Motion Tracking Algorithm Based on FPGA

2014 ◽  
Vol 926-930 ◽  
pp. 3302-3305 ◽  
Author(s):  
Dong Ming Liu ◽  
Chao Liu ◽  
Hai Wei Mu
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Video Processing ◽  
Motion Tracking ◽  
Tracking Algorithm ◽  
Development System ◽  
The Real ◽  
Time Motion ◽  
Processing Platform

With the FPGA technology progressing, the speed, the internal multiplier and the internal RAM of the FPGA are increasing. Its internal resource can be allocated flexibility, and there is no limit on the pipeline stages, so it is more suitable for real-time video processing comparing with the previous DSP and PC. By this reason, the DE2 development system is selected as the real-time video processing platform, which has a core of the Cyclone II series FPGA, in which the calculation of LK-algorithm-based real-time optical flow is implemented. Finally, by the reasonable overall arrangement for the pipeline and the sub-pipeline, the system achieves the real-time video motion tracking for the 640×480 resolution 30 frames/s images.

scholarly journals Accelerating Haze Removal Algorithm Using CUDA

2020 ◽  
Vol 13 (1) ◽  
pp. 85
Author(s):  
Xianyun Wu ◽  
Keyan Wang ◽  
Yunsong Li ◽  
Kai Liu ◽  
Bormin Huang
Keyword(s):  
Real Time ◽  
Flicker Noise ◽  
Processing Unit ◽  
High Definition ◽  
Real Time Processing ◽  
Time Processing ◽  
The Real ◽  
Haze Removal ◽  
High Definition Video ◽  
Atmospheric Light

The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel computing method for the real-time processing of high-definition video haze removal. First, based on the memory access pattern, we propose a simple but effective filter method called transposed filter combined with the fast local minimum filter algorithm and integral image algorithm. The proposed method successfully accelerates the parallel minimum filter algorithm and the parallel mean filter algorithm. Meanwhile, we adopt the inter-frame atmospheric light constraint to suppress the flicker noise in the video haze removal and simplify the estimation of atmospheric light. Experimental results show that our implementation can process the 1080p video sequence with 167 frames per second. Compared with single thread Central Processing Units (CPU) implementation, the speedup is up to 226× with asynchronous stream processing and qualified for the real-time high definition video haze removal.

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

scholarly journals Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

2020 ◽  
Vol 32 ◽  
pp. 03054
Author(s):  
Akshata Parab ◽  
Rashmi Nagare ◽  
Omkar Kolambekar ◽  
Parag Patil
Keyword(s):  
Real Time ◽  
Visually Impaired ◽  
Graphics Processing Unit ◽  
Human Perception ◽  
Processing Unit ◽  
Surrounding Environment ◽  
Visually Impaired People ◽  
Impaired People ◽  
Definition Of ◽  
Graphics Processing

Vision is one of the very essential human senses and it plays a major role in human perception about surrounding environment. But for people with visual impairment their definition of vision is different. Visually impaired people are often unaware of dangers in front of them, even in familiar environment. This study proposes a real time guiding system for visually impaired people for solving their navigation problem and to travel without any difficulty. This system will help the visually impaired people by detecting the objects and giving necessary information about that object. This information may include what the object is, its location, its precision, distance from the visually impaired etc. All these information will be conveyed to the person through audio commands so that they can navigate freely anywhere anytime with no or minimal assistance. Object detection is done using You Only Look Once (YOLO) algorithm. As the process of capturing the video/images and sending it to the main module has to be carried at greater speed, Graphics Processing Unit (GPU) is used. This will help in enhancing the overall speed of the system and will help the visually Impaired to get the maximum necessary instructions as quickly as possible. The process starts from capturing the real time video, sending it for analysis and processing and get the calculated results. The results obtained from analysis are conveyed to user by means of hearing aid. As a result by this system the blind or the visually impaired people can visualize the surrounding environment and travel freely from source to destination on their own.

Real-time motion tracking in image-guided oral implantology

2008 ◽  
Vol 4 (4) ◽  
pp. 339-347 ◽  
Author(s):  
Xiaojun Chen ◽  
Yanping Lin ◽  
Yiqun Wu ◽  
Chengtao Wang
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Image Guided ◽  
Time Motion ◽  
Oral Implantology
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