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2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)最新文献

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Spacial Elliptical Model for Extended Target Tracking Using Laser Measurements 激光测量扩展目标跟踪的空间椭圆模型
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916634
Hosam Alqaderi, F. Govaers, R. Schulz
{"title":"Spacial Elliptical Model for Extended Target Tracking Using Laser Measurements","authors":"Hosam Alqaderi, F. Govaers, R. Schulz","doi":"10.1109/SDF.2019.8916634","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916634","url":null,"abstract":"This paper presents an approach for tracking extended targets using measurements from Light Detection and Ranging (LiDAR), where the measurement's source is distributed on the contour of the target extent. An Extended Kalman Filter (EKF) is used to estimate the target kinematic and shape simultaneously. The target shape is represented by an ellipse and the measurement's source is distributed uniformly on the contour of the ellipse. A Gaussian mixture is used to approximate the measurement likelihood. The EKF update incorporates a moment matching technique to approximate the mixture density. Data from an Ibeo LiDAR simulator is used to evaluate the performance of the proposed approach on tracking a target with a rectangular shape.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134355404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Computer Vision Methods for Automating High Temperature Steel Section Sizing in Thermal Images 热图像中高温型钢截面尺寸自动化的计算机视觉方法
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916635
Peng Wang, Yueda Lin, R. Muroiwa, S. Pike, L. Mihaylova
{"title":"Computer Vision Methods for Automating High Temperature Steel Section Sizing in Thermal Images","authors":"Peng Wang, Yueda Lin, R. Muroiwa, S. Pike, L. Mihaylova","doi":"10.1109/SDF.2019.8916635","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916635","url":null,"abstract":"This paper proposes a solution to autonomously measuring steel sections with images captured by a monocular, uncalibrated thermal camera. A fast structural random forest algorithm extracts the edges of the steel sections from sequentially coming image data. Two approaches are proposed that recognize the edges and remotely evaluate the size of the manufacturing objects of interest, which will facilitate automating the steel manufacturing process. Four sets of experiments are conducted, and the results show that our method achieves accurate dimension measuring results, with a root mean square error less than 2.5 mm, which is the maximum tolerance bound of the manufacturing process.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116029219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Passive emitter direction finding using a single antenna and compressed sensing 使用单天线和压缩感知的被动辐射源测向
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916650
Markus Krestel, Folker Hoffmann, Hans Schily, A. Charlish, Sven Rau
{"title":"Passive emitter direction finding using a single antenna and compressed sensing","authors":"Markus Krestel, Folker Hoffmann, Hans Schily, A. Charlish, Sven Rau","doi":"10.1109/SDF.2019.8916650","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916650","url":null,"abstract":"Using recent advances exploiting compressed sensing (CS) theory for radio frequency emitter localization a simple one antenna direction finder has been constructed in order to develop and evaluate theses methods. A series of experiments has been conducted whose results are presented in this paper. Furthermore, the parametrization of the algorithm is analyzed and evaluated. The experiments confirm that CS techniques enhance the accuracy of bearing measurements compared to other methods while at the same time the overall hardware system can be kept simple.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116354106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Derivation of the discrete-time constant turn rate and acceleration motion model 离散恒转速和加速度运动模型的推导
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916654
Daniel Svensson
{"title":"Derivation of the discrete-time constant turn rate and acceleration motion model","authors":"Daniel Svensson","doi":"10.1109/SDF.2019.8916654","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916654","url":null,"abstract":"For vehicle tracking in automotive applications there are a number of proposed motion models. One of those models is the constant turn rate and acceleration (CTRA) model. In the original paper where the model was introduced, the state prediction function was defined, but not the process noise. In this paper, a derivation of the process noise is made. For completeness, the discrete-time prediction model is also derived, using linearized discretization.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117020299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Multi-Model Bayesian Kriging for Urban Traffic State Prediction 城市交通状态多模型贝叶斯克里格预测
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916655
K. Offor, Peng Wang, L. Mihaylova
{"title":"Multi-Model Bayesian Kriging for Urban Traffic State Prediction","authors":"K. Offor, Peng Wang, L. Mihaylova","doi":"10.1109/SDF.2019.8916655","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916655","url":null,"abstract":"In the commonly used Kriging approaches, the covariance function depends only on the separation distance irrespective of the traffic at the considered locations. A key limitation of such an approach is its inability to capture well the traffic dynamics and transitions between different states. This paper proposes a Bayesian Kriging approach for the prediction of urban traffic. The approach can capture these dynamics and model changes via the covariance matrix. The main novelty consists in representing both stationary and nonstationary changes in traffic flows by a discriminative covariance function conditioned on the observation at each location. An advantage of the approach is that it can represent congested regions and interactions in both upstream and downstream areas. Experiment carried out with real data from Santander, Spain shows that RMSE of our method outperforms traditional Kriging method by 8.4%","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115545718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparison of Track to Track fusion methods for nonlinear process and measurement models 非线性过程和测量模型中航迹与航迹融合方法的比较
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916652
Muhammad Altamash Khan
{"title":"Comparison of Track to Track fusion methods for nonlinear process and measurement models","authors":"Muhammad Altamash Khan","doi":"10.1109/SDF.2019.8916652","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916652","url":null,"abstract":"Automotive sensors play a vital role in the environment perception for vehicles in advanced driver assistance systems (ADAS). Sensors have their own distinctive advantages and drawbacks, which makes it imperative to fuse information from disparate sources. The fusion can be performed either at the sensor or the track level. Track to track fusion (T2TF) offers a big advantage as individual sensor blocks can be treated as grey or even black boxes i.e. a very limited knowledge of their characteristics might be required. In this paper, we study T2TF for a single target vehicle, tracked by two generic sensors, differing in kinematic tracking accuracy. A challenging reference trajectory is simulated consisting of both linear and nonlinear motion segments. The main objective is to compare the performance of different nonlinear sensor fusion algorithms, comprising of several combinations of prediction and measurement update methods. We show that the covariance intersection based update methods outperform the Kalman filter derivatives, as they tend not to produce overly optimistic estimates.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"1069 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116296087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
On Independent Axes Estimation for Extended Target Tracking 扩展目标跟踪的独立轴估计
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916660
F. Govaers
{"title":"On Independent Axes Estimation for Extended Target Tracking","authors":"F. Govaers","doi":"10.1109/SDF.2019.8916660","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916660","url":null,"abstract":"The trend towards high resolution sensos in combination with a growing number of automotive applications where precise estimates of dense near-range objects are required, results in an enormous need for high performance algorithms for tracking extended targets. Conventionally, this is soved by an ellipse shape approximation of the object extent. In this paper a novel method to estimate the shape parameters of an ellipse using multiple measurements is proposed. By means of an Eigenvalue Decomposition of the measurement spread matrix, the half axis can be measured. A Gaussian model for the feature observations is derived. The performance and consistency is shown by means of Monte Carlo simulations in comparison to state-of-the-art methods in literature.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection 一种基于深度学习的目标检测雷达与相机传感器融合架构
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916629
Felix Nobis, Maximilian Geisslinger, Markus Weber, Johannes Betz, M. Lienkamp
{"title":"A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection","authors":"Felix Nobis, Maximilian Geisslinger, Markus Weber, Johannes Betz, M. Lienkamp","doi":"10.1109/SDF.2019.8916629","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916629","url":null,"abstract":"Object detection in camera images, using deep learning has been proven successfully in recent years. Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles. Nevertheless, the sensor quality of the camera is limited in severe weather conditions and through increased sensor noise in sparsely lit areas and at night. Our approach enhances current 2D object detection networks by fusing camera data and projected sparse radar data in the network layers. The proposed CameraRadarFusion Net (CRF-Net) automatically learns at which level the fusion of the sensor data is most beneficial for the detection result. Additionally, we introduce BlackIn, a training strategy inspired by Dropout, which focuses the learning on a specific sensor type. We show that the fusion network is able to outperform a state-of-the-art image-only network for two different datasets. The code for this research will be made available to the public at: https://github.com/TUMFTM/CameraRadarFusionNet","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"725 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120867408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 161
Leveraging Uncertainty in Adversarial Learning to Improve Deep Learning Based Segmentation 利用对抗学习中的不确定性来改进基于深度学习的分割
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) Pub Date : 2019-10-01 DOI: 10.1109/SDF.2019.8916632
Mahed Javed, L. Mihaylova
{"title":"Leveraging Uncertainty in Adversarial Learning to Improve Deep Learning Based Segmentation","authors":"Mahed Javed, L. Mihaylova","doi":"10.1109/SDF.2019.8916632","DOIUrl":"https://doi.org/10.1109/SDF.2019.8916632","url":null,"abstract":"This paper proposes a new framework that combines Bayesian SegNet with adversarial learning to obtain high-quality segmented objects of interest. The proposed architecture takes in the form of two discriminator networks that are trained separately. The first network discriminates between segmentation maps coming either from the SegNet or the ground truth. The second network discriminates between the model uncertainty obtained from SegNet and an ideal solution that does not include uncertainty. The process is very similar to the fusion of sensor information for better decision making. Uncertainty is considered as a measure of mistakes. Hence, learning from it will help improve the performance of neural networks. Our results show that we obtain higher accuracies compared to Bayesian SegNet. Training is performed on a small-sized dataset called CamVid and a large-sized dataset Sun RGB-D. The paper shows that dealing with uncertainties is beneficial for decision making in neural networks, especially in applications with highly uncertain environments. Examples include self-driving cars and medical imaging in cancer treatment.","PeriodicalId":186196,"journal":{"name":"2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125825316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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