2021 IEEE 24th International Conference on Information Fusion (FUSION)最新文献_第9页

Video Anomaly Detection for Surveillance Based on Effective Frame Area 基于有效帧面积的监控视频异常检测

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626932

Yuxing Yang, Yang Xian, Zeyu Fu, S. M. Naqvi

引用次数: 2

Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario 工业厂房内快速故障检测的时空决策融合:石油和天然气场景

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626941

Gianluca Tabella, D. Ciuonzo, N. Paltrinieri, P. Rossi

引用次数: 1

Riemannian Lp Center of Mass for Scatter Matrix Estimation in Complex Elliptically Symmetric Distributions 复杂椭圆对称分布中散射矩阵估计的黎曼Lp质心

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626967

Mengjiao Tang, Yao Rong, Chen Chen

{"title":"Riemannian Lp Center of Mass for Scatter Matrix Estimation in Complex Elliptically Symmetric Distributions","authors":"Mengjiao Tang, Yao Rong, Chen Chen","doi":"10.23919/fusion49465.2021.9626967","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626967","url":null,"abstract":"A popular method for fusing a set of covariance matrix estimates (with unavailable correlation) is to solve their geometrical mean or median, which is defined by a Riemannian geometry of Hermitian positive-definite (HPD) matrices. The most well-known such geometry is identical to the Fisher information geometry of multivariate Gaussian distributions with a fixed mean. This paper identifies the space of HPD matrices with the manifold of centered (i.e., zero-mean) complex elliptically symmetric (CES) distributions. First, the Fisher information matrix for the CES distributions defines a different Riemannian metric on HPD matrices, and the induced Riemannian geometry is studied. Then, the Riemannian Lp mean of some HPD matrices is calculated to produce a final estimation for the scatter matrix (proportional to the covariance matrix) of a CES distribution. While the corresponding objective function is proven to be gconvex, a Riemannian gradient descent algorithm is given to compute the solution. Finally, numerical examples are provided to illustrate the derived geometrical structure and its application to target detection.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124818702","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

Variance Guided Continual Learning in a Convolutional Neural Network Gaussian Process Single Classifier Approach for Multiple Tasks in Noisy Images 方差引导下的卷积神经网络高斯过程单分类器持续学习

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626907

Mahed Javed, L. Mihaylova, N. Bouaynaya

{"title":"Variance Guided Continual Learning in a Convolutional Neural Network Gaussian Process Single Classifier Approach for Multiple Tasks in Noisy Images","authors":"Mahed Javed, L. Mihaylova, N. Bouaynaya","doi":"10.23919/fusion49465.2021.9626907","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626907","url":null,"abstract":"This work provides a continual learning solution in a single-classifier to multiple classification tasks with various data sets. A Gaussian process (GP) is combined with a Convolutional Neural Network (CNN) feature extractor architecture (CNNGP). Post softmax samples are used to estimate the variance. The variance is characterising the impact of uncertainties and is part of the update process for the learning rate parameters. Within the proposed framework two learning approaches are adopted: 1) in the first, the weights of the CNN are deterministic and only the GP learning rate is updated, 2) in the second setting, prior distributions are adopted for the CNN weights. Both the learning rates of the CNN and the GP are updated. The algorithm is trained on two variants of the MNIST dataset, split-MNIST and permuted-MNIST. Results are compared with the Uncertainty Guided Continual Bayesian Networks (UCB) multi-classifier approach [1]. The validation shows that the proposed algorithm in the Bayesian setting outperforms the UCB in tasks subject to Gaussian noise image noises and shows robustness.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114133413","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

Bathymetry and Atomic Gravimetry Sensor Fusion for Autonomous Underwater Vehicle 自主水下航行器的测深与原子重力传感器融合

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626893

Camille Palmier, K. Dahia, Nicolas Merlinge, D. Laneuville, P. Moral

引用次数: 0

Data fusion for georeferencing a laser scanner based multi-sensor system in a city environment 城市环境中基于激光扫描仪的多传感器系统的地理参考数据融合

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627026

Dominik Ernst, Jan Jüngerink, Leon Kindervater, Rozhin Moftizadeh, H. Alkhatib, S. Vogel

引用次数: 0

Joint Calibration and Direct Position Determination for Moving Array Sensors 移动阵列传感器的联合标定与直接定位

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626965

Jannik Springer, M. Oispuu, W. Koch

引用次数: 1

Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter 杂波中间歇可见目标跟踪的后验cram<s:1> - rao界

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626856

M. Hernandez, M. J. Ransom, S. Maskell

{"title":"Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter","authors":"M. Hernandez, M. J. Ransom, S. Maskell","doi":"10.23919/fusion49465.2021.9626856","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626856","url":null,"abstract":"In this paper, the posterior Cramér-Rao bound (PCRB) is determined for a scenario in which a single target is only intermittently visible to a multi-sensor system, with visibility governed by a discrete time Markovian switching system. The scenario also allows for missed detections and false alarms. Two PCRB methodologies are developed. The first approach adjusts the probability of detection to account for the a-priori probability that a target is visible, resulting in a time-dependent \"information reduction factor\" that degrades the measurement contribution accordingly. The second approach determines a conditional PCRB by sampling potential visibility/non-visibility sequences, and then calculates an unconditional bound as a weighted average. The resulting PCRBs are compared to the performance of an integrated expected likelihood particle filter (IELPF) and an integrated probabilistic data association filter (IPDAF),for scenarios with one or two sensors, and a range of clutter densities. It is shown that there is good agreement between the best performing filter and the PCRBs in the one sensor scenarios. However, when a second high-rate sensor is added to the system, the filter performance is similar to the bound only when the clutter density is low, with the PCRBs shown to be optimistic when the clutter density is high.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113989316","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

Single-target density for tracking indistinguishable objects 单目标密度跟踪难以区分的目标

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626882

M. Ulmke

{"title":"Single-target density for tracking indistinguishable objects","authors":"M. Ulmke","doi":"10.23919/fusion49465.2021.9626882","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626882","url":null,"abstract":"The concept of indistinguishability in multi-target tracking leads to correlations in their statistical description even without explicit interactions between the objects. These correlations can be described in terms of a wave function – the square-root of the multi-target probability density function (pdf) – which is necessarily either symmetric or anti-symmetric under the exchange of two target indices. [1] This symmetry dichotomy, well-known in quantum many particle physics as bosonic and fermionic behavior, leads to specific properties of the multi-target pdf. While anti-symmetry results in a repulsive behavior in terms of the single object states, symmetry leads to clustering of single objects into the same state. This different behavior can be exploited to describe macroscopic objects which either tend to avoid each other or to form groups.In this paper, we develop an approach for tracking multiple non-interacting indistinguishable targets in the presence of false alarms. The goal is to avoid the treatment of the high-dimensional multi-target pdf by approximating it in terms of the square of so-called Slater determinants and permanents build from single target pdfs. From the intensity (first order statistical moment) of the multi-target pdf, we derive approximations for single target pdfs which show the specific fermionic and bosonic behavior. These \"corrected\" single target pdfs can serve as input into standard data association and filtering algorithms. Exemplary implementations in a JPDAF framework demonstrate the mitigation of track coalescence.","PeriodicalId":226850,"journal":{"name":"2021 IEEE 24th International Conference on Information Fusion (FUSION)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180759","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

An Elliptical Principal Axes-based Model for Extended Target Tracking with Marine Radar Data 基于椭圆主轴的舰船雷达扩展目标跟踪模型

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627039

Jaya Shradha Fowdur, M. Baum, F. Heymann

引用次数: 4