Ultra Low Latency Visual Servoing For High Speed Object Tracking Using Multi Focal Length Camera Arrays
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Ultra Low Latency Visual Servoing For High Speed Object Tracking Using Multi Focal Length Camera Arrays
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Author : Alexander Steven McCown
language : en
Publisher:
Release Date : 2019
Ultra Low Latency Visual Servoing For High Speed Object Tracking Using Multi Focal Length Camera Arrays written by Alexander Steven McCown and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Electronic dissertations categories.
In high speed applications of visual servoing, latency from the recognition algorithm can cause significant degradation of in response time. Hardware acceleration allows for recognition algorithms to be applied directly during the raster scan from the image sensor, thereby removing virtually all video processing latency. This paper examines one such method, along with an analysis of design decisions made to optimize for use during high speed airborne object tracking tests for the US military. Designing test equipment for defense use involves working around unique challenges that arise from having many details being deemed classified or highly sensitive information. Designing tracking system without knowing any exact numbers for speeds, mass, distance or nature of the objects being tracked requires a flexible control system that can be easily tuned after installation. To further improve accuracy and allow rapid tuning to a yet undisclosed set of parameters, a machine learning powered auto-tuner is developed and implemented as a control loop optimizer.
Multi Camera Uncalibrated Visual Servoing
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Author : Matthew Q. Marshall
language : en
Publisher:
Release Date : 2013
Multi Camera Uncalibrated Visual Servoing written by Matthew Q. Marshall and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Control theory categories.
Uncalibrated visual servoing (VS) can improve robot performance without needing camera and robot parameters. Multiple cameras improve uncalibrated VS precision, but no works exist simultaneously using more than two cameras. The first data for uncalibrated VS simultaneously using more than two cameras are presented. VS performance is also compared for two different camera models: a high-cost camera and a low-cost camera, the difference being image noise magnitude and focal length. A Kalman filter based control law for uncalibrated VS is introduced and shown to be stable under the assumptions that robot joint level servo control can reach commanded joint offsets and that the servoing path goes through at least one full column rank robot configuration. Adaptive filtering by a covariance matching technique is applied to achieve automatic camera weighting, prioritizing the best available data. A decentralized sensor fusion architecture is utilized to assure continuous servoing with camera occlusion. The decentralized adaptive Kalman filter (DAKF) control law is compared to a classical method, Gauss-Newton, via simulation and experimentation. Numerical results show that DAKF can improve average tracking error for moving targets and convergence time to static targets. DAKF reduces system sensitivity to noise and poor camera placement, yielding smaller outliers than Gauss-Newton. The DAKF system improves visual servoing performance, simplicity, and reliability.
Visual Servoing For Robotic Positioning And Tracking Systems
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Author : Yimin Zhao
language : en
Publisher:
Release Date : 2012
Visual Servoing For Robotic Positioning And Tracking Systems written by Yimin Zhao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Taking Mobile Multi Object Tracking To The Next Level
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Author : Dennis Mitzel
language : en
Publisher:
Release Date : 2014
Taking Mobile Multi Object Tracking To The Next Level written by Dennis Mitzel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Automatic tracking categories.
Recent years have seen considerable progress in automotive safety and autonomous navigation applications, fueled by the remarkable advance of individual Computer Vision components, such as object detection, tracking, stereo and visual odometry. The goal in such applications is to automatically infer semantic understanding from the environment, observed from a moving vehicle equipped with a camera system. The pedestrian detection and tracking components constitute an actively researched part in scene understanding, important for safe navigation, path planning, and collision avoidance. Classical tracking-by-detection approaches require a robust object detector that needs to be executed in every frame. However, the detector is typically the most computationally expensive component, especially if more than one object class needs to be detected. A first goal of this thesis was to develop a vision system based on stereo camera input that is able to detect and track multiple pedestrians in real-time. To this end, we propose a hybrid tracking system that combines a computationally cheap low-level tracker with a more complex high-level tracker. The low-level trackers are either based on level-set segmentation or stereo range data together with a point registration algorithm and are employed in order to follow individual pedestrians over time, starting from an initial object detection. In order to cope with drift and to bridge occlusions that cannot be resolved by low-level trackers, the resulting tracklet outputs are fed to a high-level multihypothesis tracker, which performs longer-term data association. With this integration we obtain a real-time tracking framework by reducing object detector applications to fewer frames or even to few small image regions when stereo data is available. Reduction of expensive detector evaluations is especially relevant for the deployment on mobile platforms, where real-time performance is crucial and computational resources are notoriously
Object Tracking Implementation On Fpga Platform Using Cmos Camera And Servo Motors
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Author : FNU Hardik
language : en
Publisher:
Release Date : 2022
Object Tracking Implementation On Fpga Platform Using Cmos Camera And Servo Motors written by FNU Hardik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Object Tracking using Computer Vision has been a highly discussed topic within the AI and ML community for a while now, but most hardware real-time applications within this realm have long been reliant on having either high-speed dedicated sequential processors for processing the huge amount of calculations necessary or a real-time embedded processor setup which can work faster than traditional sequential processors but still have some of their limitations. Ideally for a real-time application we want our system to have as much parallel signal processing capability as possible. Hence, to leverage the much faster signal processing capability this project adopts a Verilog based HDL implementation on the Zedboard FPGA platform. This project proposes a low-cost design for real-time object tracking implementation on the Zedboard FPGA. The algorithm used to achieve this implementation works in a parallel processing configuration for minimum latency and uses thresholding techniques to detect color and geometric centroid calculations to track positions for objects of interest. The project is extended and implemented on a pan/tilt mechanical module having positional servo motors to control X and Y directional movement that will serve as a mount for the CMOS OV7670 Pmod Camera for complete real time object tracking. This project has potential in computer vision related applications. The hardware design files made using Verilog for this project are implemented using Vivado IDE provided by Xilinx.
Monocular Model Based 3d Tracking Of Rigid Objects
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Author : Vincent Lepetit
language : en
Publisher: Now Publishers Inc
Release Date : 2005
Monocular Model Based 3d Tracking Of Rigid Objects written by Vincent Lepetit and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
Monocular Model-Based 3D Tracking of Rigid Objects reviews the different techniques and approaches that have been developed by industry and research.
Object Distance Measurement Using A Single Camera For Robotic Applications
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Author : Peyman Alizadeh
language : en
Publisher:
Release Date : 2015
Object Distance Measurement Using A Single Camera For Robotic Applications written by Peyman Alizadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Visual servoing is defined as controlling robots by extracting data obtained from the vision system, such as the distance of an object with respect to a reference frame, or the length and width of the object. There are three image-based object distance measurement techniques: i) using two cameras, i.e., stereovision; ii) using a single camera, i.e., monovision; and iii) time-of-flight camera. The stereovision method uses two cameras to find the object's depth and is highly accurate. However, it is costly compared to the monovision technique due to the higher computational burden and the cost of two cameras (rather than one) and related accessories. In addition, in stereovision, a larger number of images of the object need to be processed in real-time, and by increasing the distance of the object from cameras, the measurement accuracy decreases. In the time-of-flight distance measurement technique, distance information is obtained by measuring the total time for the light to transmit to and reflect from the object. The shortcoming of this technique is that it is difficult to separate the incoming signal, since it depends on many parameters such as the intensity of the reflected light, the intensity of the background light, and the dynamic range of the sensor. However, for applications such as rescue robot or object manipulation by a robot in a home and office environment, the high accuracy distance measurement provided by stereovision is not required. Instead, the monovision approach is attractive for some applications due to: i) lower cost and lower computational burden; and ii) lower complexity due to the use of only one camera. Using a single camera for distance measurement, object detection and feature extraction (i.e., finding the length and width of an object) is not yet well researched and there are very few published works on the topic in the literature. Therefore, using this technique for real-world robotics applications requires more research and improvements. This thesis mainly focuses on the development of object distance measurement and feature extraction algorithms using a single fixed camera and a single camera with variable pitch angle based on image processing techniques. As a result, two different improved and modified object distance measurement algorithms were proposed for cases where a camera is fixed at a given angle in the vertical plane and when it is rotating in a vertical plane. In the proposed algorithms, as a first step, the object distance and dimension such as length and width were obtained using existing image processing techniques. Since the results were not accurate due to lens distortion, noise, variable light intensity and other uncertainties such as deviation of the position of the object from the optical axes of camera, in the second step, the distance and dimension of the object obtained from existing techniques were modified in the X- and Y-directions and for the orientation of the object about the Z-axis in the object plane by using experimental data and identification techniques such as the least square method. Extensive experimental results confirmed that the accuracy increased for measured distance from 9.4 mm to 2.95 mm, for length from 11.6 mm to 2.2 mm, and for width from 18.6 mm to 10.8 mm. In addition, the proposed algorithm is significantly improved with proposed corrections compared to existing methods. Furthermore, the improved distance measurement method is computationally efficient and can be used for real-time robotic application tasks such as pick and place and object manipulation in a home or office environment.
Visual Control Of Robots
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Author : Peter I. Corke
language : en
Publisher: Taylor & Francis Group
Release Date : 1996
Visual Control Of Robots written by Peter I. Corke and has been published by Taylor & Francis Group this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Technology & Engineering categories.
Visual Object Tracking In Dynamic Scenes
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Author : Mohamed Hamed Abdelpakey
language : en
Publisher:
Release Date : 2021
Visual Object Tracking In Dynamic Scenes written by Mohamed Hamed Abdelpakey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Visual object tracking is a fundamental task in the field computer vision. Visual object tracking is widely used in numerous applications which include, but are not limited to video surveillance, image understanding, robotics, and human-computer interaction. In essence, visual object tracking is the problem of estimating the states/trajectory of the object of interest over time. Unlike other tasks such as object detection where the number of classes/categories are defined beforehand, the only available information of the object of interest is at the first frame. Even though, Deep Learning (DL) has revolutionised most computer vision tasks, visual object tracking still imposes several challenges. The nature of visual object tracking task is stochastic, where no prior-knowledge is available about the object of interest during the training or testing/inference. Moreover, visual object tracking is a class-agnostic task, as opposed object detection and segmentation tasks. In this thesis, the main objective is to develop and advance the visual object trackers using novel designs of deep learning frameworks and mathematical formulations. To take advantage of different trackers, a novel framework is developed to track moving objects based on a composite framework and a reporter mechanism. The composite framework has built-in trackers and user-defined trackers to track the object of interest. The framework contains a module to calculate the robustness for each tracker and a reporter mechanism serves as a recovery mechanism if trackers fail to locate the object of interest. Different trackers may fail to track the object of interest, thus, a more robust framework based on Siamese network architecture, namely DensSiam, is proposed to use the concept of dense layers and connects each dense layer in the network to all layers in a feed-forward fashion with a similarity-learning function. DensSiam also includes a Self-Attention mechanism to force the network to pay more attention to non-local features during offline training. Generally, Siamese trackers do not fully utilize semantic and objectness information from pre-trained networks that have been trained on an image classification task. To solve this problem a novel architecture design is proposed , dubbed DomainSiam, to learn a Domain-Aware that fully utilizes semantic and objectness information while producing a class-agnostic track using a ridge regression network. Moreover, to reduce the sparsity problem, we solve the ridge regression problem with a differentiable weighted-dynamic loss function. Siamese trackers have high speed and work in real-time, however, they lack high accuracy. To overcome this challenge, a novel dynamic policy gradient Agent-Environment architecture with Siamese network (DP-Siam) is proposed to train the tracker to increase the accuracy and the expected average overlap while running in real-time. DP-Siam is trained offline with reinforcement learning to produce a continuous action that predicts the optimal object location. One of the common design block in most object trackers in the literature is the backbone network, where the backbone network is trained in the feature space. To design a backbone network that maps from feature space to another space (i.e., joint-nullspace) and more suitable for object tracking and classification, a novel framework is proposed. The new framework is called NullSpaceNet has a clear interpretation for the feature representation and the features in this space are more separable. NullSpaceNet is utilized in object tracking by regularizing the discriminative joint-nullspace backbone network. The novel tracker is called NullSpaceRDAR, and encourages the network to have a representation for the target-specific information for the object of interest in the joint-nullspace. In contrast to feature space where objects from a specific class are categorized into one category however, it is insensitive to intra-class variations. Furthermore, we use the NullSpaceNet backbone to learn a tracker, dubbed NullSpaceRDAR, with a regularized discriminative joint-nullspace backbone network that is specifically designed for object tracking. In the regularized discriminative joint-nullspace, the features from the same target-specific are collapsed into one point in the joint-null space and different targetspecific features are collapsed into different points in the joint-nullspace. Consequently, the joint-nullspace forces the network to be sensitive to the variations of the object from the same class (intra-class variations). Moreover, a dynamic adaptive loss function is proposed to select the suitable loss function from a super-set family of losses based on the training data to make NullSpaceRDAR more robust to different challenges.
Robotics Vision And Control
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Author : Peter Corke
language : en
Publisher: Springer
Release Date : 2011-09-05
Robotics Vision And Control written by Peter Corke and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-05 with Technology & Engineering categories.
The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals of robot kinematics, dynamics and joint level control, then camera models, image processing, feature extraction and epipolar geometry, and bring it all together in a visual servo system. Additional material is provided at http://www.petercorke.com/RVC