scholarly journals Development and implementation of the method for high resolution object tracking in 3D space

2021 ◽  
Author(s):  
Spiros Jason Hippolyte
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Optimal Solution ◽  
Depth Estimation ◽  
Matlab Simulation ◽  
Optimal Method ◽  
Ground Vehicle ◽  
3D Space ◽  
Memory Resources ◽  
Xilinx Fpga

The means to track objects in 3D space is paramount to computer vision and robotics. Improving upon prior work of the M.A.R.S. project enabled more accurate object tracking and ranging, required investigation into current techniques of stereo depth estimation, object tracking algorithms and the use of FPGA platforms. The research focused on aviation, ground vehicle and robotic applications of stereo computer vision and image processing methods. The implementation of the project design focused on how to obtain greater disparity resolution from the stereo system while minimizing memory resources. The analysis of the optimal method and then the coding and debugging of the optimal solution was performed to insure inter-operability with the existing system and lay the foundation for further expansion of the system. Comparative analysis of Xilinx FPGA platforms and MATLAB simulation of the concept provided data on hardware resources, improved disparity output and the minimal use of memory.

scholarly journals Development and implementation of the method for high resolution object tracking in 3D space

2021 ◽  
Author(s):  
Spiros Jason Hippolyte
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Optimal Solution ◽  
Depth Estimation ◽  
Matlab Simulation ◽  
Optimal Method ◽  
Ground Vehicle ◽  
3D Space ◽  
Memory Resources ◽  
Xilinx Fpga

The means to track objects in 3D space is paramount to computer vision and robotics. Improving upon prior work of the M.A.R.S. project enabled more accurate object tracking and ranging, required investigation into current techniques of stereo depth estimation, object tracking algorithms and the use of FPGA platforms. The research focused on aviation, ground vehicle and robotic applications of stereo computer vision and image processing methods. The implementation of the project design focused on how to obtain greater disparity resolution from the stereo system while minimizing memory resources. The analysis of the optimal method and then the coding and debugging of the optimal solution was performed to insure inter-operability with the existing system and lay the foundation for further expansion of the system. Comparative analysis of Xilinx FPGA platforms and MATLAB simulation of the concept provided data on hardware resources, improved disparity output and the minimal use of memory.

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

Trajectory tracking and point stability of three-axis aero-dynamic pendulum with MPC strategy in disturbance environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Liu ◽  
Jiahong Xu ◽  
Yuhong Liu
Keyword(s):  
Predictive Model ◽  
Trajectory Tracking ◽  
Control Strategy ◽  
Kinematic Model ◽  
Control Variable ◽  
Optimal Solution ◽  
Intelligent Manufacturing ◽  
Pendulum System ◽  
Content Type ◽  
3D Space

Purpose The purpose of this research on the control of three-axis aero-dynamic pendulum with disturbance is to facilitate the applications of equipment with similar pendulum structure in intelligent manufacturing and robot. Design/methodology/approach The controller proposed in this paper is mainly implemented in the following ways. First, the kinematic model of the three-axis aero-dynamic pendulum is derived in state space form to construct the predictive model. Then, according to the predictive model and objective function, the control problem can be expressed a quadratic programming (QP) problem. The optimal solution of the QP problem at each sampling time is the value of control variable. Findings The trajectory tracking and point stability tests performed on the 3D space with different disturbances are validated and the results show the effectiveness of the proposed control strategy. Originality/value This paper proposes a nonlinear unstable three-axis aero-dynamic pendulum with less power devices. Meanwhile, the trajectory tracking and point stability problem of the pendulum system is investigated with the model predictive control strategy.

scholarly journals Linear programming measures for solving the problem of a linear division of convex multiplayers.

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Sarmad H. Ali ◽  
Osamah A. Ali ◽  
Samir C. Ajmi
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Optimal Solution ◽  
Optimal Method ◽  
Linear Separation ◽  
Different Types ◽  
Simplex Methods

In this research, we are trying to solve Simplex methods which are used for successively improving solution and finding the optimal solution, by using different types of methods Linear, the concept of linear separation is widely used in the study of machine learning, through this study we will find the optimal method to solve by comparing the time consumed by both Quadric and Fisher methods.

scholarly journals Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chunlei Xia ◽  
Longwen Fu ◽  
Zuoyi Liu ◽  
Hui Liu ◽  
Lingxin Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Aquatic Toxicity ◽  
Warning System ◽  
Video Tracking ◽  
Behavioral Data ◽  
3D Space ◽  
Biological Early Warning System ◽  
Abnormal Pattern ◽  
2D And 3D

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

scholarly journals Latent 3D Volume for Joint Depth Estimation and Semantic Segmentation from a Single Image

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5765 ◽  
Author(s):  
Seiya Ito ◽  
Naoshi Kaneko ◽  
Kazuhiko Sumi
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Depth Estimation ◽  
Image Features ◽  
Feature Representation ◽  
Single Image ◽  
Feature Vectors ◽  
3D Space ◽  
3D Volume

This paper proposes a novel 3D representation, namely, a latent 3D volume, for joint depth estimation and semantic segmentation. Most previous studies encoded an input scene (typically given as a 2D image) into a set of feature vectors arranged over a 2D plane. However, considering the real world is three-dimensional, this 2D arrangement reduces one dimension and may limit the capacity of feature representation. In contrast, we examine the idea of arranging the feature vectors in 3D space rather than in a 2D plane. We refer to this 3D volumetric arrangement as a latent 3D volume. We will show that the latent 3D volume is beneficial to the tasks of depth estimation and semantic segmentation because these tasks require an understanding of the 3D structure of the scene. Our network first constructs an initial 3D volume using image features and then generates latent 3D volume by passing the initial 3D volume through several 3D convolutional layers. We apply depth regression and semantic segmentation by projecting the latent 3D volume onto a 2D plane. The evaluation results show that our method outperforms previous approaches on the NYU Depth v2 dataset.

scholarly journals Estimation for Motion in Tracking and Detection Objects with Kalman Filter

2020 ◽  
Author(s):  
Afef Salhi ◽  
Fahmi Ghozzi ◽  
Ahmed Fakhfakh
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Visual Motion ◽  
Optimal Solution ◽  
Block Matching ◽  
Detection And Tracking ◽  
Tracking Object ◽  
Tracking Objects ◽  
Localization And Tracking ◽  
Prediction Parameters

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

scholarly journals Greedy Algorithm for Minimizing the Cost of Routing Power on a Digital Microgrid

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3076 ◽  
Author(s):  
Zhengqi Jiang ◽  
Vinit Sahasrabudhe ◽  
Ahmed Mohamed ◽  
Haim Grebel ◽  
Roberto Rojas-Cessa
Keyword(s):  
Optimal Solution ◽  
High Complexity ◽  
Distributed Energy ◽  
Optimal Method ◽  
Cost Rate ◽  
Trade Offs ◽  
Lower Complexity ◽  
Cost Paths ◽  
The Cost ◽  
Selection Of

In this paper, we propose the greedy smallest-cost-rate path first (GRASP) algorithm to route power from sources to loads in a digital microgrid (DMG). Routing of power from distributed energy resources (DERs) to loads of a DMG comprises matching loads to DERs and the selection of the smallest-cost-rate path from a load to its supplying DERs. In such a microgrid, one DER may supply power to one or many loads, and one or many DERs may supply the power requested by a load. Because the optimal method is NP-hard, GRASP addresses this high complexity by using heuristics to match sources and loads and to select the smallest-cost-rate paths in the DMG. We compare the cost achieved by GRASP and an optimal method based on integer linear programming on different IEEE test feeders and other test networks. The comparison shows the trade-offs between lowering complexity and achieving optimal-cost paths. The results show that the cost incurred by GRASP approaches that of the optimal solution by small margins. In the adopted networks, GRASP trades its lower complexity for up to 18% higher costs than those achieved by the optimal solution.

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