scholarly journals A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2397
Author(s):  
Aarav Pandya ◽  
Ajit Jha ◽  
Linga Reddy Cenkeramaddi
Keyword(s):  
Optical Flow ◽  
Autonomous Agents ◽  
Feature Fusion ◽  
Target Object ◽  
Velocity Estimation ◽  
Machine Learning Techniques ◽  
Design Data ◽  
Monocular Camera ◽  
Dynamic Estimation ◽  
Radar Calibration

Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow velocity estimation when the object of interest is at the edge of the frame or far away from the camera without requiring camera–radar calibration. Moreover, algorithms of various complexity were implemented using custom dataset, and each of them successfully detected the object and estimated its velocity accurately and independently of the object’s distance and location in frame. Here, we present a complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance. It includes setup design, data acquisition, dataset development, and finally, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.

scholarly journals Distance and velocity estimation using optical flow from a monocular camera

2017 ◽  
Vol 9 (3) ◽  
pp. 198-208 ◽  
Author(s):  
Hann Woei Ho ◽  
Guido CHE de Croon ◽  
Qiping Chu
Keyword(s):  
Optical Flow ◽  
Micro Air Vehicles ◽  
Velocity Estimation ◽  
Flight Tests ◽  
Monocular Camera ◽  
Flying Insects ◽  
Outdoor Environments ◽  
Landing Control ◽  
Vehicle Movement ◽  
Air Vehicles

Monocular vision is increasingly used in micro air vehicles for navigation. In particular, optical flow, inspired by flying insects, is used to perceive vehicle movement with respect to the surroundings or sense changes in the environment. However, optical flow does not directly provide us the distance to an object or velocity, but the ratio of them. Thus, using optical flow in control involves nonlinearity problems which add complexity to the controller. To deal with that, we propose an algorithm that estimates distance and velocity of the vehicle based on optical flow measured from a monocular camera and the knowledge of control inputs. This algorithm applies an extended Kalman filter to state estimation and uses the estimates for landing control. We implement and test our algorithm in computer simulation and on board a Parrot AR.Drone 2.0 to demonstrate its feasibility for micro air vehicles landings. Results of the simulation and multiple flight tests show that the algorithm is able to estimate height and velocity of the micro air vehicles accurately, and achieves smooth landings with these estimates, even in windy outdoor environments.

Dynamic estimation of optical flow field using objective functions

1989 ◽  
Vol 7 (4) ◽  
pp. 259-267 ◽  
Author(s):  
Zhao Wei-Zhao ◽  
Qi Fei-Hu ◽  
Young Tzay Y
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Objective Functions ◽  
Optical Flow Field ◽  
Dynamic Estimation

scholarly journals Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Shivanand S. Gornale ◽  
Sathish Kumar ◽  
Abhijit Patil ◽  
Prakash S. Hiremath
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Gender Classification ◽  
Biometric Data ◽  
Security Applications ◽  
Biometric Security ◽  
Learning Techniques ◽  
Handwritten Signature

Biometric security applications have been employed for providing a higher security in several access control systems during the past few years. The handwritten signature is the most widely accepted behavioral biometric trait for authenticating the documents like letters, contracts, wills, MOU’s, etc. for validation in day to day life. In this paper, a novel algorithm to detect gender of individuals based on the image of their handwritten signatures is proposed. The proposed work is based on the fusion of textural and statistical features extracted from the signature images. The LBP and HOG features represent the texture. The writer’s gender classification is carried out using machine learning techniques. The proposed technique is evaluated on own dataset of 4,790 signatures and realized an encouraging accuracy of 96.17, 98.72 and 100% for k-NN, decision tree and Support Vector Machine classifiers, respectively. The proposed method is expected to be useful in design of efficient computer vision tools for authentication and forensic investigation of documents with handwritten signatures.

Monocular Camera Three Dimensional Reconstruction Based on Optical Flow Feedback

2015 ◽  
Vol 35 (5) ◽  
pp. 0515001 ◽  
Author(s):  
李秀智 Li Xiuzhi ◽  
杨爱林 Yang Ailin ◽  
秦宝岭 Qin Baoling ◽  
贾松敏 Jia Songmin ◽  
邱欢 Qiu Huan
Keyword(s):  
Optical Flow ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Monocular Camera ◽  
Dimensional Reconstruction

scholarly journals Satellite Angular Velocity Estimation Based on Star Images and Optical Flow Techniques

Sensors ◽  
2013 ◽  
Vol 13 (10) ◽  
pp. 12771-12793 ◽  
Author(s):  
Giancarmine Fasano ◽  
Giancarlo Rufino ◽  
Domenico Accardo ◽  
Michele Grassi
Keyword(s):  
Angular Velocity ◽  
Optical Flow ◽  
Velocity Estimation ◽  
Angular Velocity Estimation ◽  
Star Images ◽  
Flow Techniques

scholarly journals A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yanfei Xiang ◽  
Jianbing Ma ◽  
Xi Wu
Keyword(s):  
Machine Learning ◽  
Optical Flow ◽  
Weather Prediction ◽  
Radar Data ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Model Parameters ◽  
Real World Data ◽  
Flow Method ◽  
Optical Flow Method

Unpredicted precipitations, even mild, may cause severe economic losses to many businesses. Precipitation nowcasting is hence significant for people to make correct decisions timely. For traditional methods, such as numerical weather prediction (NWP), the accuracy is limited because the smaller scale of strong convective weather must be smaller than the minimum scale that the model can capture. And it often requires a supercomputer. Furthermore, the optical flow method has been proved to be available for precipitation nowcasting. However, it is difficult to determine the model parameters because the two steps of tracking and extrapolation are separate. In contrast, current machine learning applications are based on well-selected full datasets, ignoring the fact that real datasets quite often contain missing data requiring extra consideration. In this paper, we used a real Hubei dataset in which a few radar echo data are missing and proposed a proper mechanism to deal with the situation. Furthermore, we proposed a novel mechanism for radar reflectivity data with single altitudes or cumulative altitudes using machine learning techniques. From the experimental results, we conclude that our method can predict future precipitation with a high accuracy when a few data are missing, and it outperforms the traditional optical flow method. In addition, our model can be used for various types of radar data with a type-specific feature extraction, which makes the method more flexible and suitable for most situations.

scholarly journals Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone

2017 ◽  
Vol 2 (2) ◽  
pp. 1070-1076 ◽  
Author(s):  
Kimberly McGuire ◽  
Guido de Croon ◽  
Christophe De Wagter ◽  
Karl Tuyls ◽  
Hilbert Kappen
Keyword(s):  
Optical Flow ◽  
Obstacle Avoidance ◽  
Stereo Vision ◽  
Velocity Estimation

Optical Flow-Based Monocular Vision/INS Integrated Navigation for Mobile Robot Indoors

2014 ◽  
Vol 538 ◽  
pp. 375-378 ◽  
Author(s):  
Xi Yuan Chen ◽  
Jing Peng Gao ◽  
Yuan Xu ◽  
Qing Hua Li
Keyword(s):  
Mobile Robot ◽  
Optical Flow ◽  
Global Positioning System ◽  
Autonomous Navigation ◽  
Image Sequences ◽  
Velocity Estimation ◽  
Monocular Vision ◽  
Integrated Navigation ◽  
Global Positioning ◽  
Flow Algorithm

This paper proposed a new algorithm for optical flow-based monocular vision (MV)/ inertial navigation system (INS) integrated navigation. In this mode, a downward-looking camera is used to get the image sequences, which is used to estimate the velocity of the mobile robot by using optical flow algorithm. INS is employed for the yaw variation. In order to evaluate the performance of the proposed method, a real indoor test has done. The result shows that the proposed method has good performance for velocity estimation. It can be applied to the autonomous navigation of mobile robots when the Global Positioning System (GPS) and code wheel is unavailable.

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