2021 IEEE 24th International Conference on Information Fusion (FUSION)最新文献_第2页

Bayesian Estimation with Artificial Neural Network 基于人工神经网络的贝叶斯估计

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626979

Sehyun Yun, Renato Zanetti

引用次数: 0

An Overview of Machine Learning Methods for Multiple Target Tracking 多目标跟踪的机器学习方法综述

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627045

C. Chong

引用次数: 3

Optimal Sensor Placement for Shooter Localization Using a Genetic Algorithm 基于遗传算法的射击定位传感器优化配置

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626955

Luisa Still, M. Oispuu, W. Koch

引用次数: 1

Improved Explainability through Uncertainty Estimation in Automatic Target Recognition of SAR Images 利用不确定性估计提高SAR图像自动目标识别的可解释性

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627066

N.D. Blomerus, J. D. Villiers, Willie Nel

{"title":"Improved Explainability through Uncertainty Estimation in Automatic Target Recognition of SAR Images","authors":"N.D. Blomerus, J. D. Villiers, Willie Nel","doi":"10.23919/fusion49465.2021.9627066","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627066","url":null,"abstract":"There have been numerous advancements in machine learning technologies in recent years, which has led to the application of machine learning algorithms to automatic target recognition. Two key challenges for these methods are the lack of sufficient training datasets and non-transparent deep models. In this paper, experiments are conducted that investigate the application of target detection using a model trained on the MSTAR to detect targets in another dataset, as well as the investigation of uncertainty estimates in Bayesian convolutional neural networks and how these outputs can improve confidence in the model’s predictions. The model can correctly detect targets in the test scene’s, as well as targets not seen from the MSTAR dataset. The output of the Bayesian convolutional neural network is used to create uncertainty heat maps. The epistemic uncertainty is the uncertainty created by the model and aleatoric is created by the data. These heat maps are overlaid on SAR images, thereby aiding in explainability by highlighting regions in the SAR images that exhibit high uncertainty from a classification point of view. Hence, uncertainty estimates from the Bayesian model give insight into the confidence of its predictions and show promise to improve trust between users and the model.","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":"121968734","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: 4

Tuning Multi Object Tracking Systems using Bayesian Optimization 基于贝叶斯优化的多目标跟踪系统调优

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626895

Tobias Fleck, Johann Marius Zöllner

{"title":"Tuning Multi Object Tracking Systems using Bayesian Optimization","authors":"Tobias Fleck, Johann Marius Zöllner","doi":"10.23919/fusion49465.2021.9626895","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9626895","url":null,"abstract":"Tracking-by-Detection has become the major paradigm in Multi Object Tracking (MOT) for a large variety of sensors. Regardless of the type of tracking system, hyper parameters are often chosen manually instead of doing a structured search to reveal the full potential of the system.In this work we tackle this problem by utilizing Bayesian Optimization (BO) to tune tracking systems, enabling to find the best combination of hyper parameters for Gaussian Mixture Probability Hypothesis Density Trackers (GM-PHD) in two different tracking applications. We use the Tree-structured Parzen Estimator (TPE) algorithm [1] [2] with an Expected Improvement (EI) acquisition function as a blackbox optimizer. TPE supports to conveniently incorporate domain expert knowledge by modeling prior probability distributions of the search space. In our experiments we use the popular MOTA metric as optimization objective.Evaluation is performed in a simulation scenario with an in depth discussion of the found parameters and a real world example that uses the MOT-20 challenge dataset [3] demonstrates the unconditional applicability of the approach. We finish with a conclusion on Bayesian Optimization for MOT systems and future research.","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":"128696248","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

An embedded platform approach to privacy-centric person re-identification 以隐私为中心的个人身份再识别的嵌入式平台方法

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9626896

Nicholas Pym, A. D. Freitas

引用次数: 1

Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain 面向海事领域的概率知识图自动构建研究

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/FUSION49465.2021.9626935

Fatemeh Shiri, Teresa Wang, Shirui Pan, Xiaojun Chang, Yuan-Fang Li, Gholamreza Haffari, V. Nguyen, Shuang Yu

{"title":"Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain","authors":"Fatemeh Shiri, Teresa Wang, Shirui Pan, Xiaojun Chang, Yuan-Fang Li, Gholamreza Haffari, V. Nguyen, Shuang Yu","doi":"10.23919/FUSION49465.2021.9626935","DOIUrl":"https://doi.org/10.23919/FUSION49465.2021.9626935","url":null,"abstract":"International maritime crime is becoming increasingly sophisticated, often associated with wider criminal networks. Detecting maritime threats by means of fusing data purely related to physical movement (i.e., those generated by physical sensors, or hard data) is not sufficient. This has led to research and development efforts aimed at combining hard data with other types of data (especially human-generated or soft data). Existing work often assumes that input soft data is available in a structured format, or is focused on extracting certain relevant entities or concepts to accompany or annotate hard data. Much less attention has been given to extracting the rich knowledge about the situations of interest implicitly embedded in the large amount of soft data existing in unstructured formats (such as intelligence reports and news articles). In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts, but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i.e., in the form of probabilistic knowledge graphs). This will increase the accuracy of, and confidence in, the extracted knowledge and facilitate subsequent reasoning and learning. To this end, we propose Maritime DeepDive, an initial prototype for the automated construction of probabilistic knowledge graphs from natural language data for the maritime domain. In this paper, we report on the current implementation of Maritime DeepDive, together with preliminary results on extracting probabilistic events from maritime piracy incidents. This pipeline was evaluated on a manually crafted gold standard, yielding promising results.","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":"132508589","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

Real-time Activation Pattern Monitoring and Uncertainty Characterisation in Image Classification 图像分类中的实时激活模式监测与不确定度表征

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627071

Shenglin Wang, Peng Wang, L. Mihaylova, Matthew Hill

{"title":"Real-time Activation Pattern Monitoring and Uncertainty Characterisation in Image Classification","authors":"Shenglin Wang, Peng Wang, L. Mihaylova, Matthew Hill","doi":"10.23919/fusion49465.2021.9627071","DOIUrl":"https://doi.org/10.23919/fusion49465.2021.9627071","url":null,"abstract":"Deep neural networks (DNNs) have become very popular recently and have proven their potential especially for image classification. However, their performance depends significantly on the network structure and data quality. This paper investigates the performance of DNNs and especially of faster region based convolutional neural networks (R-CNNs), called faster R-CNN when the network testing data differ significantly from the training data. This paper proposes a framework for monitoring the neuron patterns within a faster R-CNN by representing distributions of neuron activation patterns and by calculating corresponding distances between them, with the Kullback-Leibler divergence. The patterns of the activation states of ‘neurons’ within the network can therefore be observed if the faster R-CNN is ‘outside the comfort zone’, mostly when it works with noisy data and data that are significantly different from those used in the training stage. The validation is performed on publicly available datasets: MNIST [1] and PASCAL [2] and demonstrates that the proposed framework can be used for real-time monitoring of supervised classifiers.","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":"128027468","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

GPS and IMU Fusion for Human Gait Estimation GPS和IMU融合用于人体步态估计

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627008

J. J. Steckenrider, Brock Crawford, Penny Zheng

引用次数: 2

Combining LSTM and MDN Networks for traffic forecasting using the Argoverse Dataset 结合LSTM和MDN网络使用Argoverse数据集进行流量预测

2021 IEEE 24th International Conference on Information Fusion (FUSION) Pub Date : 2021-11-01 DOI: 10.23919/fusion49465.2021.9627009

David Schwab, Sean M. O’Rourke, Breton L. Minnehan

引用次数: 0