The Multi-object Tracking Based on Gradient and Depth Information in the Stereo Vision

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.

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A Stereo-Vision Based Object Tracking Approach at Roundabouts

IEEE Intelligent Transportation Systems Magazine ◽

10.1109/mits.2013.2244934 ◽

2013 ◽

Vol 5 (2) ◽

pp. 22-32 ◽

Cited By ~ 16

Author(s):

M. Muffert ◽

D. Pfeiffer ◽

U. Franke

Keyword(s):

Object Tracking ◽

Stereo Vision

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A multiple object tracking approach that combines colour and depth information using a confidence measure

Pattern Recognition Letters ◽

10.1016/j.patrec.2008.03.004 ◽

2008 ◽

Vol 29 (10) ◽

pp. 1504-1514 ◽

Cited By ~ 19

Author(s):

Rafael Muñoz-Salinas ◽

Eugenio Aguirre ◽

Miguel García-Silvente ◽

Antonio Gonzalez

Keyword(s):

Object Tracking ◽

Multiple Object Tracking ◽

Depth Information ◽

Confidence Measure ◽

Multiple Object

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Autonomous stereo vision system for depth computation of moving object

10.32920/ryerson.14661516.v1 ◽

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.

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