Depth information on moving objects using stereo vision

1997 ◽  
Author(s):  
Ik Soo Choy ◽  
Yonggil Sin ◽  
Jong-An Park
Keyword(s):  
Stereo Vision ◽  
Moving Objects ◽  
Depth Information

scholarly journals Stereo Vision Based Robot for Remote Monitoring with VR Support

2019 ◽  
Vol 9 (1S3) ◽  
pp. 79-84
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Stereo Vision ◽  
Deep Neural Networks ◽  
Moving Objects ◽  
Video Stream ◽  
Head Movements ◽  
Depth Information ◽  
Stereoscopic Video ◽  
Cloud Server

The machine vision systems have been playing a significant role in visual monitoring systems. With the help of stereovision and machine learning, it will be able to mimic human-like visual system and behaviour towards the environment. In this paper, we present a stereo vision based 3-DOF robot which will be used to monitor places from remote using cloud server and internet devices. The 3-DOF robot will transmit human-like head movements, i.e., yaw, pitch, roll and produce 3D stereoscopic video and stream it in Real-time. This video stream is sent to the user through any generic internet devices with VR box support, i.e., smartphones giving the user a First-person real-time 3D experience and transfers the head motion of the user to the robot also in Real-time. The robot will also be able to track moving objects and faces as a target using deep neural networks which enables it to be a standalone monitoring robot. The user will be able to choose specific subjects to monitor in a space. The stereovision enables us to track the depth information of different objects detected and will be used to track human interest objects with its distances and sent to the cloud. A full working prototype is developed which showcases the capabilities of a monitoring system based on stereo vision, robotics, and machine learning.

Depth Information Enhancement Using Block Matching and Image Pyramiding Stereo Vision Enabled RGB-D Sensor

2020 ◽  
Vol 20 (10) ◽  
pp. 5406-5414 ◽  
Author(s):  
Sunil Jacob ◽  
Varun G. Menon ◽  
Saira Joseph
Keyword(s):  
Stereo Vision ◽  
Block Matching ◽  
Depth Information

Research on People Counting Based on Stereo Vision

2013 ◽  
Vol 373-375 ◽  
pp. 619-623
Author(s):  
Yun Zhou Zhang ◽  
Shou Shuai Xu ◽  
Liang Gao ◽  
Shan Bao Yang ◽  
Xiao Lin Su
Keyword(s):  
Stereo Vision ◽  
Moving Objects ◽  
Feature Matching ◽  
Object Segmentation ◽  
Vision System ◽  
Office Building ◽  
Disparity Map ◽  
Passenger Flow ◽  
The People ◽  
Binocular Vision System

In both industrial field and office building, the accurate statistic of people who enter or leave the elevator has important practical meaning in security and analysis of passenger flow. We present a binocular vision system to count the people pass by. The camera is set in the height of 2.45 meters to monitor the people overhead in order to reduce the overlap of pedestrians. The object segment and tracking method proposed in this paper show good result with the disparity map gained by the dual-camera. Dynamic promotion of threshold is used in the object segmentation. Feature matching is used to track the moving objects. The system can get the number of people accurate and timely. Experiment results show that our system has good performance under relatively complex circumstance.

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.

Depth Illusion by Delayed 3-D Perception (‘Delayed Stereopsis Illusion’): A Novel Way to Determine Computation Times in Human Vision by Depth Reversal in Partially Occluded Moving Objects

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 181-181 ◽  
Author(s):  
R Wolf ◽  
M Schuchardt ◽  
R Rosenzweig
Keyword(s):  
Moving Objects ◽  
Human Vision ◽  
Model Systems ◽  
Depth Information ◽  
Motion Direction ◽  
Processing Times ◽  
Velocity Motion ◽  
Partially Occluded ◽  
Measurable Distance ◽  
Binocular Depth

Viewed through depth-reversing spectacles, nontransparent objects appear to cut ‘gaps’ into a patterned background. In moving objects this gap is seen to extend beyond the occluded area (‘delayed stereopsis illusion’, DSI): Its trailing border appears to lag behind by a precisely measurable distance, indicating a processing time of approximately 0.13 s to accomplish stereopsis [cf Morgan and Castet, 1995 Nature (London)378 380 – 383]. Other than in thigmaesthesia, there is no correction by antedating. Why is this delay not perceived in normal stereopsis? If an object is moving before some background, the background usually maintains its position; it may be occluded, or not. Depth information thus might be extrapolated to the continuously uncovered regions of the patterned background. Depth reversal demands that the occluded region of the background must jump behind the moving, occluding object. As this object is perceived to retain its distance, the background, as it is getting uncovered, must jump back into the foreground, where it can be perceived only after renewed calculation of binocular depth. The dependence of DSI on eye movements, disparity, velocity, motion direction, surface texture, illuminance, spatial frequency, and fractal dimension of the objects involved is currently being investigated in model systems which allow us to determine processing times of human stereopsis under well-defined conditions.

Sign in / Sign up

Export Citation Format

Share Document