scholarly journals Drone Detection and Pose Estimation Using Relational Graph Networks

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1479 ◽  
Author(s):  
Ren Jin ◽  
Jiaqi Jiang ◽  
Yuhua Qi ◽  
Defu Lin ◽  
Tao Song
Keyword(s):  
Pose Estimation ◽  
Optical Sensors ◽  
State Of The Art ◽  
Estimation Algorithm ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Estimation Algorithms ◽  
Low Altitude ◽  
Real Scenario ◽  
Keypoints Detection

With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accurate pose annotation or a 3D target model, which costs a lot of human resource and is difficult to apply to non-cooperative targets. To overcome these problems, a quadrotor 6D pose estimation algorithm was proposed in this paper. It was based on keypoints detection (only need keypoints annotation), relational graph network and perspective-n-point (PnP) algorithm, which achieves state-of-the-art performance both in simulation and real scenario. In addition, the inference ability of our relational graph network to the keypoints of four motors was also evaluated. The accuracy and speed were improved significantly compared with the state-of-the-art keypoints detection algorithm.

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

2007 ◽  
Vol 111 (1120) ◽  
pp. 389-396 ◽  
Author(s):  
G. Campa ◽  
M. R. Napolitano ◽  
M. Perhinschi ◽  
M. L. Fravolini ◽  
L. Pollini ◽  
...  
Keyword(s):  
Machine Vision ◽  
Pose Estimation ◽  
Estimation Algorithm ◽  
Computational Effort ◽  
Unmanned Aircraft ◽  
Light Sources ◽  
Estimation Algorithms ◽  
Trade Offs ◽  
Labeling Algorithms ◽  
Selection Of

Abstract This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

scholarly journals Model-Based 3D Pose Estimation of a Single RGB Image Using a Deep Viewpoint Classification Neural Network

2019 ◽  
Vol 9 (12) ◽  
pp. 2478 ◽  
Author(s):  
Jui-Yuan Su ◽  
Shyi-Chyi Cheng ◽  
Chin-Chun Chang ◽  
Jing-Ming Chen
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Training Dataset ◽  
3D Pose Estimation ◽  
Estimation Algorithms ◽  
Model Based ◽  
3D Scene ◽  
Rgb Image

This paper presents a model-based approach for 3D pose estimation of a single RGB image to keep the 3D scene model up-to-date using a low-cost camera. A prelearned image model of the target scene is first reconstructed using a training RGB-D video. Next, the model is analyzed using the proposed multiple principal analysis to label the viewpoint class of each training RGB image and construct a training dataset for training a deep learning viewpoint classification neural network (DVCNN). For all training images in a viewpoint class, the DVCNN estimates their membership probabilities and defines the template of the class as the one of the highest probability. To achieve the goal of scene reconstruction in a 3D space using a camera, using the information of templates, a pose estimation algorithm follows to estimate the pose parameters and depth map of a single RGB image captured by navigating the camera to a specific viewpoint. Obviously, the pose estimation algorithm is the key to success for updating the status of the 3D scene. To compare with conventional pose estimation algorithms which use sparse features for pose estimation, our approach enhances the quality of reconstructing the 3D scene point cloud using the template-to-frame registration. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets and compare it with the state-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods in terms of the accuracy of pose estimation.

scholarly journals Using DeepLabCut for 3D markerless pose estimation across species and behaviors

2018 ◽  
Author(s):  
Tanmay Nath ◽  
Alexander Mathis ◽  
An Chi Chen ◽  
Amir Patel ◽  
Matthias Bethge ◽  
...  
Keyword(s):  
Pose Estimation ◽  
User Interfaces ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Estimation Algorithm ◽  
Graphical User Interfaces ◽  
Training Data ◽  
Behavioral Tracking ◽  
Human Pose ◽  
Python Package

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.

scholarly journals From IR Images to Point Clouds to Pose: Point Cloud-Based AR Glasses Pose Estimation

2021 ◽  
Vol 7 (5) ◽  
pp. 80
Author(s):  
Ahmet Firintepe ◽  
Carolin Vey ◽  
Stylianos Asteriadis ◽  
Alain Pagani ◽  
Didier Stricker
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
State Of The Art ◽  
Estimation Error ◽  
Point Clouds ◽  
Intermediate Representation ◽  
Infrared Images ◽  
Estimation Algorithms ◽  
3D Point Clouds ◽  
Model Combining

In this paper, we propose two novel AR glasses pose estimation algorithms from single infrared images by using 3D point clouds as an intermediate representation. Our first approach “PointsToRotation” is based on a Deep Neural Network alone, whereas our second approach “PointsToPose” is a hybrid model combining Deep Learning and a voting-based mechanism. Our methods utilize a point cloud estimator, which we trained on multi-view infrared images in a semi-supervised manner, generating point clouds based on one image only. We generate a point cloud dataset with our point cloud estimator using the HMDPose dataset, consisting of multi-view infrared images of various AR glasses with the corresponding 6-DoF poses. In comparison to another point cloud-based 6-DoF pose estimation named CloudPose, we achieve an error reduction of around 50%. Compared to a state-of-the-art image-based method, we reduce the pose estimation error by around 96%.

scholarly journals Can Markerless Pose Estimation Algorithms Estimate 3D Mass Centre Positions and Velocities during Linear Sprinting Activities?

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2889
Author(s):  
Laurie Needham ◽  
Murray Evans ◽  
Darren P. Cosker ◽  
Steffi L. Colyer
Keyword(s):  
Pose Estimation ◽  
Poor Performance ◽  
Estimation Algorithm ◽  
Specific Data ◽  
Estimation Algorithms ◽  
Mass Centre ◽  
Kalman Smoothing ◽  
The Mean ◽  
Human Pose ◽  
Signal Processing Methods

The ability to accurately and non-invasively measure 3D mass centre positions and their derivatives can provide rich insight into the physical demands of sports training and competition. This study examines a method for non-invasively measuring mass centre velocities using markerless human pose estimation and Kalman smoothing. Marker (Qualysis) and markerless (OpenPose) motion capture data were captured synchronously for sprinting and skeleton push starts. Mass centre positions and velocities derived from raw markerless pose estimation data contained large errors for both sprinting and skeleton pushing (mean ± SD = 0.127 ± 0.943 and −0.197 ± 1.549 m·s−1, respectively). Signal processing methods such as Kalman smoothing substantially reduced the mean error (±SD) in horizontal mass centre velocities (0.041 ± 0.257 m·s−1) during sprinting but the precision remained poor. Applying pose estimation to activities which exhibit unusual body poses (e.g., skeleton pushing) appears to elicit more erroneous results due to poor performance of the pose estimation algorithm. Researchers and practitioners should apply these methods with caution to activities beyond sprinting as pose estimation algorithms may not generalise well to the activity of interest. Retraining the model using activity specific data to produce more specialised networks is therefore recommended.

scholarly journals A Novel Algorithm for Detecting Pedestrians on Rainy Image

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 112
Author(s):  
Yuhang Liu ◽  
Jianxiao Ma ◽  
Yuchen Wang ◽  
Chenhong Zong
Keyword(s):  
State Of The Art ◽  
Pedestrian Detection ◽  
Heavy Rain ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Traffic Data ◽  
Average Precision ◽  
Synthetic Datasets ◽  
Light Medium

Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.

scholarly journals Exposing Speech Transsplicing Forgery with Noise Level Inconsistency

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Diqun Yan ◽  
Mingyu Dong ◽  
Jinxing Gao
Keyword(s):  
Change Point ◽  
Estimation Algorithm ◽  
Detection Algorithm ◽  
Noise Signal ◽  
Change Point Detection ◽  
Detection Methods ◽  
Signal To Noise ◽  
Original Speech ◽  
Point Detection ◽  
Frequency Features

Splicing is one of the most common tampering techniques for speech forgery in many forensic scenarios. Some successful approaches have been presented for detecting speech splicing when the splicing segments have different signal-to-noise ratios (SNRs). However, when the SNRs between the spliced segments are close or even same, no effective detection methods have been reported yet. In this study, noise inconsistency between the original speech and the inserted segment from other speech is utilized to detect the splicing trace. First, noise signal of the suspected speech is extracted by a parameter-optimized noise estimation algorithm. Second, the statistical Mel frequency features are extracted from the estimated noise signal. Finally, the spliced region is located by utilizing a change point detection algorithm on the estimated noise signal. The effectiveness of the proposed method is evaluated on a well-designed speech splicing dataset. The comparative experimental results show that the proposed algorithm can achieve better detection performance than other algorithms.

scholarly journals Intelligent Spacecraft Visual GNC Architecture With the State-Of-the-Art AI Components for On-Orbit Manipulation

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhou Hao ◽  
R. B. Ashith Shyam ◽  
Arunkumar Rathinam ◽  
Yang Gao
Keyword(s):  
Decision Making ◽  
Pose Estimation ◽  
Degrees Of Freedom ◽  
State Of The Art ◽  
Robot Manipulator ◽  
Estimation Algorithm ◽  
Ground Control ◽  
Robot Arm ◽  
Wide Range ◽  
Human Operators

Conventional spacecraft Guidance, Navigation, and Control (GNC) architectures have been designed to receive and execute commands from ground control with minimal automation and autonomy onboard spacecraft. In contrast, Artificial Intelligence (AI)-based systems can allow real-time decision-making by considering system information that is difficult to model and incorporate in the conventional decision-making process involving ground control or human operators. With growing interests in on-orbit services with manipulation, the conventional GNC faces numerous challenges in adapting to a wide range of possible scenarios, such as removing unknown debris, potentially addressed using emerging AI-enabled robotic technologies. However, a complete paradigm shift may need years' efforts. As an intermediate solution, we introduce a novel visual GNC system with two state-of-the-art AI modules to replace the corresponding functions in the conventional GNC system for on-orbit manipulation. The AI components are as follows: (i) A Deep Learning (DL)-based pose estimation algorithm that can estimate a target's pose from two-dimensional images using a pre-trained neural network without requiring any prior information on the dynamics or state of the target. (ii) A technique for modeling and controlling space robot manipulator trajectories using probabilistic modeling and reproduction to previously unseen situations to avoid complex trajectory optimizations on board. This also minimizes the attitude disturbances of spacecraft induced on it due to the motion of the robot arm. This architecture uses a centralized camera network as the main sensor, and the trajectory learning module of the 7 degrees of freedom robotic arm is integrated into the GNC system. The intelligent visual GNC system is demonstrated by simulation of a conceptual mission—AISAT. The mission is a micro-satellite to carry out on-orbit manipulation around a non-cooperative CubeSat. The simulation shows how the GNC system works in discrete-time simulation with the control and trajectory planning are generated in Matlab/Simulink. The physics rendering engine, Eevee, renders the whole simulation to provide a graphic realism for the DL pose estimation. In the end, the testbeds developed to evaluate and demonstrate the GNC system are also introduced. The novel intelligent GNC system can be a stepping stone toward future fully autonomous orbital robot systems.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

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