2019 22th International Conference on Information Fusion (FUSION)最新文献

Multiple Model BLE-based Tracking via Validation of RSSI Fluctuations under Different Conditions 基于多模型ble的不同条件下RSSI波动验证跟踪

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011367

M. Atashi, Mohammad Salimibeni, Parvin Malekzadeh, Mihai Barbulescu, K. Plataniotis, Arash Mohammadi

{"title":"Multiple Model BLE-based Tracking via Validation of RSSI Fluctuations under Different Conditions","authors":"M. Atashi, Mohammad Salimibeni, Parvin Malekzadeh, Mihai Barbulescu, K. Plataniotis, Arash Mohammadi","doi":"10.23919/fusion43075.2019.9011367","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011367","url":null,"abstract":"Of particular interest to this paper is indoor positioning via integration of information fusion, localization, and tracking technologies with Internet of Things (IoT) devices equipped with sensing, processing, and Bluetooth Low Energy (BLE) communication capabilities. In particular, the objective is development of advanced signal processing and machine learning solutions to micro-locate and track a person within a delimited physical space (e.g. building) using BLE locating infrastructure installed within this space. In this regard and as the first step, the paper focuses on evaluation and validation of RSSI fluctuations under different environmental conditions. Therefore, the first goal of the paper is to implement a Location-Based Services (LBS) platform consisting of two main sub-systems, i.e., acquisition sub-system, and the Fusion Centre (FC). The second goal of the paper is to test and validate effects of different parameters on the RSSI values and on tracking performance. Based on real experiments, the implemented LBS platform shows potential capabilities for incorporation of different fusion frameworks and providing accurate tracking results.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124374125","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 New Network Structure for Semantic Segmentation of Ship Targets in Remote Sensing 一种新的遥感船舶目标语义分割网络结构

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011331

Cai Mi, Cui Yaqi, Lv Yafei, Z. Jing, Xiong Wei, Jiazheng Pei

{"title":"A New Network Structure for Semantic Segmentation of Ship Targets in Remote Sensing","authors":"Cai Mi, Cui Yaqi, Lv Yafei, Z. Jing, Xiong Wei, Jiazheng Pei","doi":"10.23919/fusion43075.2019.9011331","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011331","url":null,"abstract":"Accurate detection of ship targets is a research hotspot in computer vision. Most of the researches have achieved instance-level detection in the way of bounding box. But we intend to achieve more accurate detection of ship targets in pixel-level through semantic segmentation. However, there are still two main challenges: the first one is the difficulty to segment small targets caused by the difference among ship targets' scales, and the other one is the lack of localization information caused by insufficient recovery ability of the decoder part. In this paper, we propose an effective solution. First, a multi-scale pooling fusion module is proposed to fuse multi-scale feature maps and acquire more multi-scale context information, then we improve the capability of precise decoding by taking the place of convolution operation with deconvolution in the decoder part to gather more localization information. At last, we integrate above two schemes into an encoder-decoder symmetry training network with less training parameters and less training time. Furthermore, we construct a dataset for ship semantic segmentation called HRSC2016-SS by labeling HRSC2016 dataset to evaluate our solution. Experiments show that comparing with the existing methods, our proposed solution has a better performance.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121102802","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

Autonomous Heart Rate Tracking Methodology Using Kalman Filter and the EM Algorithm 基于卡尔曼滤波和EM算法的自主心率跟踪方法

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011407

T. Souza, B. Balasingam, R. Maev

{"title":"Autonomous Heart Rate Tracking Methodology Using Kalman Filter and the EM Algorithm","authors":"T. Souza, B. Balasingam, R. Maev","doi":"10.23919/fusion43075.2019.9011407","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011407","url":null,"abstract":"Accurate heart rate monitoring during intense physical activity is a challenging problem due to high levels of motion artifacts (MA) in sensors that rely on stable physical proximity/contact for accurate measurement extraction. Photo-plethysmography (PPG) sensor is a non-invasive optical sensor that is widely used in wearable devices, such as smartwatches, to measure blood volume changes using the property of light reflection and absorption; these measurements can be used to extract the heart rate (HR) of an individual wearing that device. The PPG sensor is susceptible to the motion artifact which increases with physical activity. Since the frequency of the motion artifact is very close to the range of HR, estimation of HR information becomes very challenging. As a result, MA removal remains an active research topic over the last few years. Several approaches have been developed in the recent past for MA removal and accurate HR estimation. Among these recent works, a Kalman Filter (KF) based approach showed promising results for accurate estimation and tracking of HR based on PPG measurements. However, the previous KF based HR tracker was demonstrated for a particular dataset with manually tuned filter parameters. Such a custom tuned approach might not perform accurately in practical scenarios where the amount of motion artifact and the heart-rate variability depend on numerous, unpredictable factors. In this paper, we develop an approach to automatically tune the KF based HR tracker based on the expectation maximization (EM) algorithm. The applicability of the proposed approach is demonstrated using an open-source PPG database that was recorded during varying pre-determined physical activities.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114379857","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

Distribution-Dependent Distance of First Two Moments 前两个矩的分布相关距离

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011168

X. Li

引用次数: 0

How to Evaluate High Level Fusion Algorithms? 如何评估高水平融合算法?

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011206

C. Laudy, N. Museux

引用次数: 3

Target Localization and Sensor Synchronization in the Presence of Data Association Uncertainty 数据关联不确定性下的目标定位与传感器同步

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011321

Tongyu Ge, R. Tharmarasa, Bernard Lebel, M. Florea, T. Kirubarajan

引用次数: 1

Dynamic Target Driven Trajectory Planning using RRT* 动态目标驱动轨迹规划使用RRT*

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011157

Simon Williams, Xuezhi Wang, D. Angley, C. Gilliam, B. Moran, Richard Ellem, T. Jackson, A. Bessell

{"title":"Dynamic Target Driven Trajectory Planning using RRT*","authors":"Simon Williams, Xuezhi Wang, D. Angley, C. Gilliam, B. Moran, Richard Ellem, T. Jackson, A. Bessell","doi":"10.23919/fusion43075.2019.9011157","DOIUrl":"https://doi.org/10.23919/fusion43075.2019.9011157","url":null,"abstract":"In this paper, we focus on dynamic trajectory planning for an autonomous underwater vehicle (AUV). Specifically, we are interested in planning the trajectory of an AUV as it returns to a moving recovery vessel. To aid in this task, the AUV is equipped with a passive, angle-only, sensor to enable localization of the recovery vessel. Accordingly, we present an algorithm that is capable of dynamically updating the trajectory of the AUV given measurement data from the passive sensor. Our approach is based on adapting a static trajectory planning algorithm from robotics, known as Rapidly-exploring Random Tree (RRT*), to allow for localization and tracking of a dynamic target (i.e. the recovery vessel). In contrast to dynamic programming or fixed grid trajectory planning, the RRT* offers a computationally efficient method for long-term trajectory planning with probabilistic guarantees of optimality. In this framework, we explore two options: trajectory planning based on minimising the distance to the target; and trajectory planning based on maximising the tracking accuracy of the target using an information theoretic cost. Using AUV recovery as an evaluation scenario, we analyse and evaluate the proposed trajectory planning algorithm against traditional dynamic programming methods. In particular, we consider trajectory planning in noisy and obstructed environments.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129582650","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

Simplification of Multi-Criteria Decision-Making Using Inter-Criteria Analysis and Belief Functions 基于准则间分析和信念函数的多准则决策简化

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011326

J. Dezert, A. Tchamova, Deqiang Han, J. Tacnet

引用次数: 5

Uncertain Visual Target Tracking by Hierarchical Combination of Multiple Target State Space Model and Self Organizing Map 多目标状态空间模型与自组织映射分层组合的不确定视觉目标跟踪

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011332

N. Ikoma

引用次数: 0

Sensor Scheduling Under Action Dependent Decision-Making Epochs 动作依赖决策时代下的传感器调度

2019 22th International Conference on Information Fusion (FUSION) Pub Date : 2019-07-01 DOI: 10.23919/fusion43075.2019.9011443

D. Raihan, W. Faber, S. Chakravorty, I. Hussein

引用次数: 1