Wavelet Analysis Based 3-D Vision System for Object Tracking

2006 ◽  
Author(s):  
Tao Dai ◽  
Xiang Chen
Keyword(s):  
Wavelet Analysis ◽  
Object Tracking ◽  
Vision System

scholarly journals Monocular Robust Depth Estimation Vision System for Robotic Tasks Interventions in Metallic Targets

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3220 ◽  
Author(s):  
Carlos Veiga Almagro ◽  
Mario Di Castro ◽  
Giacomo Lunghi ◽  
Raúl Marín Prades ◽  
Pedro José Sanz Valero ◽  
...  
Keyword(s):  
Object Tracking ◽  
Vision System ◽  
Nuclear Research ◽  
Depth Estimation ◽  
Work Load ◽  
Manual Control ◽  
Repetitive Tasks ◽  
Autonomous Mode ◽  
Robotic Tasks ◽  
Tracking And Recognition

Robotic interventions in hazardous scenarios need to pay special attention to safety, as in most cases it is necessary to have an expert operator in the loop. Moreover, the use of a multi-modal Human-Robot Interface allows the user to interact with the robot using manual control in critical steps, as well as semi-autonomous behaviours in more secure scenarios, by using, for example, object tracking and recognition techniques. This paper describes a novel vision system to track and estimate the depth of metallic targets for robotic interventions. The system has been designed for on-hand monocular cameras, focusing on solving lack of visibility and partial occlusions. This solution has been validated during real interventions at the Centre for Nuclear Research (CERN) accelerator facilities, achieving 95% success in autonomous mode and 100% in a supervised manner. The system increases the safety and efficiency of the robotic operations, reducing the cognitive fatigue of the operator during non-critical mission phases. The integration of such an assistance system is especially important when facing complex (or repetitive) tasks, in order to reduce the work load and accumulated stress of the operator, enhancing the performance and safety of the mission.

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 Active Shift Attention Based Object Tracking System

2021 ◽  
Author(s):  
◽  
Aisha Ajmal
Keyword(s):  
Object Tracking ◽  
Computational Models ◽  
Vision System ◽  
Tracking System ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Relevant Information ◽  
Human Vision ◽  
Biological Mechanisms ◽  
Salient Regions

<p>The human vision system (HVS) collects a huge amount of information and performs a variety of biological mechanisms to select relevant information. Computational models based on these biological mechanisms are used in machine vision to select interesting or salient regions in the images for application in scene analysis, object detection and object tracking.  Different object tracking techniques have been proposed often using complex processing methods. On the other hand, attention-based computational models have shown significant performance advantages in various applications. We hypothesise the integration of a visual attention model with object tracking can be effective in increasing the performance by reducing the detection complexity in challenging environments such as illumination change, occlusion, and camera moving.  The overall objective of this thesis is to develop a visual saliency based object tracker that alternates between targets using a measure of current uncertainty derived from a Kalman filter. This thesis presents the results by showing the effectiveness of the tracker using the mean square error when compared to a tracker without the uncertainty mechanism.   Specific colour spaces can contribute to the identification of salient regions. The investigation is done between the non-uniform red, green and blue (RGB) derived opponencies with the hue, saturation and value (HSV) colour space using video information. The main motivation for this particular comparison is to improve the quality of saliency detection in challenging situations such as lighting changes. Precision-Recall curves are used to compare the colour spaces using pyramidal and non-pyramidal saliency models.</p>

scholarly journals A Mirror-Based Active Vision System for Underwater Robots: From the Design to Active Object Tracking Application

2021 ◽  
Vol 8 ◽  
Author(s):  
Noel Cortés-Pérez ◽  
Luz Abril Torres-Méndez
Keyword(s):  
Object Tracking ◽  
High Speed ◽  
Vision System ◽  
Lightweight Design ◽  
Research Work ◽  
Active Vision ◽  
Easy Access ◽  
Underwater Robot ◽  
Active System ◽  
Underwater Robots

A mirror-based active system capable of changing the view’s direction of a pre-existing fixed camera is presented. The aim of this research work is to extend the perceptual tracking capabilities of an underwater robot without altering its structure. The ability to control the view’s direction allows the robot to explore its entire surroundings without any actual displacement, which can be useful for more effective motion planning and for different navigation strategies, such as object tracking and/or obstacle evasion, which are of great importance for natural preservation in environments as complex and fragile as coral reefs. Active vision systems based on mirrors had been used mainly in terrestrial platforms to capture the motion of fast projectiles using high-speed cameras of considerable size and weight, but they had not been used on underwater platforms. In this sense, our approach incorporates a lightweight design adapted to an underwater robot using affordable and easy-access technology (i.e., 3D printing). Our active system consists of two arranged mirrors, one of which remains static in front of the robot’s camera, while the orientation of the second mirror is controlled by two servomotors. Object tracking is performed by using only the pixels contained on the homography of a defined area in the active mirror. HSV color space is used to reduce lighting change effects. Since color and geometry information of the tracking object are previously known, a window filter is applied over the H-channel for color blobs detection, then, noise is filtered and the object’s centroid is estimated. If the object is lost, a Kalman filter is applied to predict its position. Finally, with this information, an image PD controller computes the servomotor articular values. We have carried out experiments in real environments, testing our active vision system in an object-tracking application where an artificial object is manually displaced on the periphery of the robot and the mirror system is automatically reconfigured to keep such object focused by the camera, having satisfactory results in real time for detecting objects of low complexity and in poor lighting conditions.

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