2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第3页

A robust gait recognition system using spatiotemporal features and deep learning 基于时空特征和深度学习的鲁棒步态识别系统

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170422

Md. Zia Uddin, W. Khaksar, J. Tørresen

引用次数: 11

A grid-based indoor radiolocation technique based on spatially coherent path loss model 一种基于空间相干路径损耗模型的网格室内无线电定位技术

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170432

Murat Ambarkutuk, T. Furukawa

引用次数: 0

An integrated UAV navigation system based on geo-registered 3D point cloud 基于地理配准三维点云的无人机综合导航系统

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170396

Shuai Zhang, Shuaijun Wang, Chengyang Li, Guocheng Liu, Qi Hao

引用次数: 2

Data association — solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME 数据关联-解决或避免:基于RFS框架和SME因子图的过滤器评估

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170349

Dhiraj Gulati, Uzair Sharif, Feihu Zhang, Daniel Clarke, A. Knoll

{"title":"Data association — solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME","authors":"Dhiraj Gulati, Uzair Sharif, Feihu Zhang, Daniel Clarke, A. Knoll","doi":"10.1109/MFI.2017.8170349","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170349","url":null,"abstract":"Data or measurement-to-track association is an integral and expensive part of any solution performing multi-target multi-sensor Cooperative Localization (CL) for better state estimation. Various performance evaluations have been performed between various state-of-the-art solutions, but they have been often limited within same family of algorithms. However, there exist solutions which avoid the task of data association to perform the CL in a multi-target multi-sensor environment. Factor Graphs using Symmetric Measurement Equations (SMEs) factor is one such solution. In this paper we compare and contrast the state estimation using state-of-the-art Random Finite Set (RFS) approach and using a Factor Graph solution with SMEs. For a RFS we use multi-sensor multi-object with the Generalized Labeled Multi-Bernoulli (GLMB) Filter. These two solution use conceptually different approaches, GLMB Filter solves the data association implicitly, but Factor Graph based solution avoids the task altogether. Simulations present an interesting results where for simple scenarios implemented GLMB filter performs efficiently. But the performance of GLMB Filter degrades faster than Factor Graphs using SMEs when the error in the sensors increase.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126227865","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

Discretization of SO(3) using recursive tesseract subdivision 用递推tesseract细分的SO(3)离散化

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170406

G. Kurz, F. Pfaff, U. Hanebeck

引用次数: 1

Fast point cloud segmentation based on flood-fill algorithm 基于洪水填充算法的快速点云分割

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170397

P. Chu, Seoungjae Cho, Y. Park, Kyungeun Cho

引用次数: 17

Light-weight semantic segmentation for compound images 复合图像的轻量级语义分割

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170444

Geonho Cha, Hwiyeon Yoo, Donghoon Lee, Songhwai Oh

{"title":"Light-weight semantic segmentation for compound images","authors":"Geonho Cha, Hwiyeon Yoo, Donghoon Lee, Songhwai Oh","doi":"10.1109/MFI.2017.8170444","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170444","url":null,"abstract":"The eye structure of insects, which is called a compound eye, has interesting advantages. It has a large field of view, low aberrations, compact size, short image processing time, and an infinite depth of field. If we can design a compound eye camera which mimics the compound eye structure of insects, compound images with these interesting advantages can be obtained. In this paper, we consider the design of a compound camera prototype and low complexity semantic segmentation scheme for compound images. The prototype has a hemisphere shape and consists of several synchronized single-lens reflex camera modules. Images captured from camera modules are mapped to compound images using multi-view geometry to emulate a compound eye. In this way, we can simulate various configurations of compound eye structures, which is useful for developing high-level applications. After that, a low complexity semantic segmentation scheme for compound images based on a convolutional neural network is proposed. The experimental result shows that compound images are more suitable for semantic segmentation than typical RGB images.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121496465","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

Experimental Study on Image Interpolation for Concealed Object Detection 图像插值用于隐藏目标检测的实验研究

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170370

Dokkyun Yi, Su-yeon Kim, S. Yeom, Mun-Kyo Lee, Sang-Won Jung

引用次数: 0

Facility activity inference using networks of radiation detectors based on SPRT 基于SPRT的辐射探测器网络的设施活动推断

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170404

N. Rao, C. Ramirez

{"title":"Facility activity inference using networks of radiation detectors based on SPRT","authors":"N. Rao, C. Ramirez","doi":"10.1109/MFI.2017.8170404","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170404","url":null,"abstract":"We consider the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility's ventilation off-gas stack. The intensity of stack emissions decays with distance, and the sensor counts or measurements are inherently random with parameters determined by the intensity at the sensor's location. We utilize the measurements to estimate the intensity at the stack, and use it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor. We demonstrate the superior performance of this method over conventional majority fusers and individual sensors using (i) test measurements from a network of 21 NaI detectors, and (ii) effluence measurements collected at the stack of a reactor facility. We also analytically establish the superior detection performance of the network over individual sensors with fixed and adaptive thresholds by utilizing the Poisson distribution of the counts. We quantify the performance improvements of the network detection over individual sensors using the packing number of the intensity space.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122864194","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

Image-based multi-target tracking using a multi-layer particle filter and extended EM clustering 基于多层粒子滤波和扩展EM聚类的图像多目标跟踪

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2017-11-01 DOI: 10.1109/MFI.2017.8170391

J. Buyer, M. Vollert, Mihai Kocsis, Nico Susmann, R. Zöllner

{"title":"Image-based multi-target tracking using a multi-layer particle filter and extended EM clustering","authors":"J. Buyer, M. Vollert, Mihai Kocsis, Nico Susmann, R. Zöllner","doi":"10.1109/MFI.2017.8170391","DOIUrl":"https://doi.org/10.1109/MFI.2017.8170391","url":null,"abstract":"The paper presents an approach for tracking a variable number of objects by using a multi-layer particle filter combined with an extended Expectation Maximization (EM) clustering. The approach works on basis of binary foreground images coming from previous background subtraction. The multi-layer particle filter is an improvement of a conventional particle filter approach. It uses an introduced adaptive layer distribution spanned over the tracking area, which determines the areal extents of the particles. Thus, the multi-modal posterior distribution representing the objects is approximated with locally different resolutions. In addition, the layer distribution is used to find new appearing objects. In order to generate an object list out of the particle density, an EM clustering is used. The basic algorithm is extended with an estimation of the needful number of clusters by iteratively splitting and comparing the overall cluster areas. The new tracking approach improves tracking quality and robustness compared to the conventional particle filter approach. Experimental results are shown using the example of a traffic scene in a roundabout.","PeriodicalId":402371,"journal":{"name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126188733","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