scholarly journals Autonomous stereo vision system for depth computation of moving object

2021 ◽  
Author(s):  
Alejandro Emerio Alfonso Oviedo
Keyword(s):  
Stereo Vision ◽  
Background Subtraction ◽  
Vision System ◽  
Moving Object ◽  
Depth Information ◽  
Processing Unit ◽  
First Order ◽  
Real World Application ◽  
Stereoscopic View ◽  
Mean Filter

This work targets one real world application of stereo vision technology: the computation of the depth information of a moving object in a scene. It uses a stereo camera set that captures the stereoscopic view of the scene. Background subtraction algorithm is used to detect the moving object, supported by the recursive filter of first order as updating method. Mean filter is the pre-processing stage, combined with frame downscaling to reduce the background storage. After thresholding the background subtraction result, the binary image is sent to the software processing unit to compute the centroid of the moving area, and the measured disparity, estimate the disparity by Kalman algorithm, and finally calculate the depth from the estimated disparity. The implementation successfully achieves the objectives of resolution 720p, at 28.68 fps and maximum permissible depth error of ±4 cm (1.066 %) for a depth measuring range from 25 cm to 375 cm.

scholarly journals Autonomous stereo vision system for depth computation of moving object

2021 ◽  
Author(s):  
Alejandro Emerio Alfonso Oviedo
Keyword(s):  
Stereo Vision ◽  
Background Subtraction ◽  
Vision System ◽  
Moving Object ◽  
Depth Information ◽  
Processing Unit ◽  
First Order ◽  
Real World Application ◽  
Stereoscopic View ◽  
Mean Filter

This work targets one real world application of stereo vision technology: the computation of the depth information of a moving object in a scene. It uses a stereo camera set that captures the stereoscopic view of the scene. Background subtraction algorithm is used to detect the moving object, supported by the recursive filter of first order as updating method. Mean filter is the pre-processing stage, combined with frame downscaling to reduce the background storage. After thresholding the background subtraction result, the binary image is sent to the software processing unit to compute the centroid of the moving area, and the measured disparity, estimate the disparity by Kalman algorithm, and finally calculate the depth from the estimated disparity. The implementation successfully achieves the objectives of resolution 720p, at 28.68 fps and maximum permissible depth error of ±4 cm (1.066 %) for a depth measuring range from 25 cm to 375 cm.

Sliding Mode Controller for Stereo Vision Based Autonomous Flight of Quad-Rotor M

2011 ◽  
Vol 23 (1) ◽  
pp. 137-148 ◽  
Author(s):  
Dwi Pebrianti ◽  
◽  
WeiWang ◽  
Daisuke Iwakura ◽  
Yuze Song ◽  
...  
Keyword(s):  
Object Tracking ◽  
Stereo Vision ◽  
Flight Control ◽  
Background Subtraction ◽  
Sliding Mode ◽  
Vision System ◽  
Sliding Mode Controller ◽  
Model Errors ◽  
Stereo Vision System ◽  
Aerial Vehicle

We have investigated the possibility of a Sliding Mode Controller (SMC) for autonomous hovering and waypoint of a quad-rotor Micro Aerial Vehicle (MAV) based on an on ground stereo vision system. The object tracking used here is running average background subtraction. Among the background subtraction algorithms for object tracking, running average is known to have the fastest processing speed and the lowest memory requirement. Stereo vision system is known to have a good performance in measuring the distance from camera to object without any information regarding the object geometry in advance. SMC is known to have advantage of insensitivity to the model errors, parametric uncertainties and other disturbances. The experiment on autonomous hovering and way-point by using running average method for object tracking and SMC for the flight control shows a reliable result.

scholarly journals Development of a Low Cost and Path-free Autonomous Patrol System Based on Stereo Vision System and Checking Flags

2020 ◽  
Vol 10 (3) ◽  
pp. 974
Author(s):  
Chien-Wu Lan ◽  
Chi-Yao Chang
Keyword(s):  
Stereo Vision ◽  
Radio Frequency Identification ◽  
Vision System ◽  
Low Cost ◽  
Color Pattern ◽  
Depth Information ◽  
Rfid Tag ◽  
Intruder Detection ◽  
Stereo Vision System ◽  
Identification Function

Nowadays, security guard patrol services are becoming roboticized. However, high construction prices and complex systems make patrol robots difficult to be popularized. In this research, a simplified autonomous patrolling robot is proposed, which is fabricated by upgrading a wheeling household robot with stereo vision system (SVS), radio frequency identification (RFID) module, and laptop. The robot has four functions: independent patrolling without path planning, checking, intruder detection, and wireless backup. At first, depth information of the environment is analyzed through SVS to find a passable path for independent patrolling. Moreover, the checkpoints made with RFID tag and color pattern are placed in appropriate positions within a guard area. While a color pattern is detected by the SVS, the patrolling robot is guided to approach the pattern and check its RFID tag. For more, the human identification function of SVS is used to detect an intruder. While a skeleton information of the human is analyzed by SVS, the intruder detection function is triggered, then the robot follows the intruder and record the images of the intruder. The recorded images are transmitted to a server through Wi-Fi to realize the remote backup, and users can query the recorded images from the network. Finally, an experiment is made to test the functions of the autonomous patrolling robot successfully.

The visual tracking system using a stereo vision robot

2014 ◽  
Vol 31 (8) ◽  
pp. 1790-1799 ◽  
Author(s):  
Long-Jyi Yeh ◽  
Tsung Han Lee ◽  
Kuei-Shu Hsu
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Vision System ◽  
Moving Object ◽  
Hand Tracking ◽  
Moving Target ◽  
Content Type ◽  
3D Environment ◽  
Stereo Vision System ◽  
Normal Environment

Purpose – The purpose of this paper is to use vision stereo to simultaneously acquire image pairs under a normal environment. Then the methods of moving edges detection and moving target shifting are applied to reduce noise error in order to position a target efficiently. The target is then double confirmed via image merge and alignment. After positioning, the visual difference between the target and the image created by the stereo vision system is measured for alignment. Finally, the image depth of the target is calculated followed by real-time target tracking. Design/methodology/approach – This study mainly applies Sobel image principle. In addition, moving edges detection and moving target shifting are also used to work with system multi-threading for improving image identification efficiency. Findings – The results of the experiment suggest that real-time image tracking and positioning under a pre-set environment can be effectively improved. On the other hand, tracking and positioning are slightly affected under a normal environment. Errors of distance measurements occur because there is more noise existing. Research limitations/implications – This study mainly determines the movements and positioning of an object or a target via image. However, the stability of moving edges detection executed by the stereo vision system can be affected if the light sources in an environment are too strong or extreme. Practical implications – So far the method of tracking and positioning a moving object has been applied to surveillance systems or the application which requires measuring and positioning under a normal environment. The method proposed by this study can also be used to construct a 3D environment. Originality/value – The method proposed by this study can also be used to construct a 3D environment or tracking moving object to measure the distance.

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