2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)最新文献_第3页

Distributed Extended Object Tracking Filter Through Embedded ADMM Technique 基于嵌入式ADMM技术的分布式扩展目标跟踪滤波器

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913876

Zhifei Li, Hongyan Wang, Shi Yan, Hongxia Zou, Mingyang Du

引用次数: 0

Encrypted Fast Covariance Intersection Without Leaking Fusion Weights 不泄漏融合权值的加密快速协方差相交

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913840

Marko Ristic, B. Noack

引用次数: 0

Event-based Driver Distraction Detection and Action Recognition 基于事件的驾驶员分心检测与动作识别

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913871

Chu Yang, Peigen Liu, Guang Chen, Zhengfa Liu, Ya Wu, Alois Knoll

{"title":"Event-based Driver Distraction Detection and Action Recognition","authors":"Chu Yang, Peigen Liu, Guang Chen, Zhengfa Liu, Ya Wu, Alois Knoll","doi":"10.1109/MFI55806.2022.9913871","DOIUrl":"https://doi.org/10.1109/MFI55806.2022.9913871","url":null,"abstract":"Driver distraction is one of the important factors leading to traffic accidents. With the development of mobile infotainment and the overestimation of immature autonomous driving technology, this phenomenon has become more and more serious. However, most existing distraction detection algorithms can not achieve satisfactory performance due to the complex in-cabin light condition and limited computing resource of edge devices. To this end, we introduce a light weight and flexible event-based system to monitor driver state. Compared with frame-based camera, the event camera responds to pixel wise light intensity changes asynchronously and has several promising advantages, including high dynamic range, high temporal resolution, low latency and low data redundant, which makes it suitable for the mobile terminal applications. The system first denoises the events stream and encode it into a sequence of 3D tensors. Then, the voxel features at different time steps are extracted using efficient net and fed into LSTM to establish temporal model, based on which, the driver distraction is detected. In addition, we extend the proposed architecture to recognise driver action and adopt transfer learning strategy to improve the detection performance. Extensive experiments are conducted on both simulated dataset (transform from Drive&Act) and real event dataset (collected by ourselves). The experimental results shows the advantages of the system on accuracy and efficient for driver state monitoring.","PeriodicalId":344737,"journal":{"name":"2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128156644","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: 3

Arithmetic Average Based Multi-sensor TPHD Filter for Distributed Multi-target Tracking 基于算术平均的多传感器TPHD滤波器用于分布式多目标跟踪

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913841

Jiazheng Fu, Lei Chai, Boxiang Zhang, Wei Yi

{"title":"Arithmetic Average Based Multi-sensor TPHD Filter for Distributed Multi-target Tracking","authors":"Jiazheng Fu, Lei Chai, Boxiang Zhang, Wei Yi","doi":"10.1109/MFI55806.2022.9913841","DOIUrl":"https://doi.org/10.1109/MFI55806.2022.9913841","url":null,"abstract":"Compared with the probability hypothesis density (PHD) filter for sets of targets, the trajectory probability hypothesis density (TPHD) filter can estimate the sets of trajectories in a principle way and has better target tracking performance. This paper aims at extending the TPHD filter to distributed multitarget tracking (MTT) for the multi-sensor system. However, in the trajectory set based distributed fusion implementation, the trajectory state difference phenomenon makes the clustering and merging techniques unfeasible in trajectory state space. To address this problem, this paper studies the space decomposition of the TPHD and proposes a distributed MTT method based on the TPHD filter with the weighted arithmetic average (WAA) fusion rule. First, we prove the rationality of the space decomposition in the posterior density of the TPHD filter. Then, based on the proposed property, we derive the WAA fusion formulation of the TPHD filter by minimizing the weighted sum of Kullback-Leibler divergences (KLD) from local posterior densities, and develop the analytical Gaussian mixture (GM) implementation with the L-scan approximation. Numerical results demonstrate the efficacy of the proposed fusion method.","PeriodicalId":344737,"journal":{"name":"2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124502277","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

PIPO: Policy Optimization with Permutation-Invariant Constraint for Distributed Multi-Robot Navigation 基于排列不变约束的分布式多机器人导航策略优化

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913862

Ruiqi Zhang, Guang Chen, Jing Hou, Zhijun Li, Alois Knoll

{"title":"PIPO: Policy Optimization with Permutation-Invariant Constraint for Distributed Multi-Robot Navigation","authors":"Ruiqi Zhang, Guang Chen, Jing Hou, Zhijun Li, Alois Knoll","doi":"10.1109/MFI55806.2022.9913862","DOIUrl":"https://doi.org/10.1109/MFI55806.2022.9913862","url":null,"abstract":"For large-scale multi-agent systems (MAS), ensuring the safety and effectiveness of navigation in complicated scenarios is a challenging task. With the agent scale increasing, most existing centralized methods lose their magic for the lack of scalability, and the popular decentralized approaches are hampered by high latency and computing requirements. In this research, we offer PIPO, a novel policy optimization algorithm for decentralized MAS navigation with permutation-invariant constraints. To conduct navigation and avoid un-necessary exploration in the early episodes, we first defined a guide-policy. Then, we introduce the permutation invariant property in decentralized multi-agent systems and leverage the graph convolution network to produce the same output under shuffled observations. Our approach can be easily scaled to an arbitrary number of agents and used in large-scale systems for its decentralized training and execution. We also provide extensive experiments to demonstrate that our PIPO significantly outperforms the baselines of multi-agent reinforcement learning algorithms and other leading methods in variant scenarios.","PeriodicalId":344737,"journal":{"name":"2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115808769","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

Enhancing Event-based Structured Light Imaging with a Single Frame 单帧增强基于事件的结构光成像

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913845

Huijiao Wang, Tangbo Liu, Chu He, Cheng Li, Jian-zhuo Liu, Lei Yu

{"title":"Enhancing Event-based Structured Light Imaging with a Single Frame","authors":"Huijiao Wang, Tangbo Liu, Chu He, Cheng Li, Jian-zhuo Liu, Lei Yu","doi":"10.1109/MFI55806.2022.9913845","DOIUrl":"https://doi.org/10.1109/MFI55806.2022.9913845","url":null,"abstract":"Benefiting from the extremely low latency, events have been used for Structured Light Imaging (SLI) to predict the depth surface. However, existing methods only focus on improving scanning speeds but neglect perturbations from event noise and timestamp jittering for depth estimation. In this paper, we build a hybrid SLI system equipped with an event camera, a high-resolution frame camera, and a digital light projector, where a single intensity frame is adopted as a guidance to enhance the event-based SLI quality. To achieve this end, we propose a Multi-Modal Feature Fusion Network (MFFN) consisting of a feature fusion module and an upscale module to simultaneously fuse events and a single intensity frame, suppress event perturbations, and reconstruct a high-quality depth surface. Further, for training MFFN, we build a new Structured Light Imaging based on Event and Frame cameras (EF-SLI) dataset collected from the hybrid SLI system, containing paired inputs composed of a set of synchronized events and one single corresponding frame, and ground-truth references obtained by a high-quality SLI approach. Experiments demonstrate that our proposed MFFN outperforms state-of-the-art event-based SLI approaches in terms of accuracy at different scanning speeds.","PeriodicalId":344737,"journal":{"name":"2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"57 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116834161","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

Probabilistic Information Matrix Fusion in a Multi-Object Environment 多目标环境下的概率信息矩阵融合

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913872

Kaipei Yang, Y. Bar-Shalom

{"title":"Probabilistic Information Matrix Fusion in a Multi-Object Environment","authors":"Kaipei Yang, Y. Bar-Shalom","doi":"10.1109/MFI55806.2022.9913872","DOIUrl":"https://doi.org/10.1109/MFI55806.2022.9913872","url":null,"abstract":"In distributed sensor fusion systems, each of the local sensors has its own tracker processing local measurements for measurement-to-track association and state estimation. Only the processed data, local tracks (LT) comprising state vector estimates and their covariance matrices are transmitted to the fusion center (FC). In this work, a multi-object hybrid probabilistic information matrix fusion (MO-HPIMF) is derived taking into account all association hypotheses. In MO-HPIMF, the association carried out is between the FC track states (prediction) and the LT state estimates from local sensors. When having a large number of objects and sensors in fusion, only the m-best FC-track-to-LT association hypotheses should be incorporated in MO-HPIMF to reduce the computational complexity. A Sequential m-best 2-D method is used for solving the multidimensional assignment problem in this work. It is shown in the simulations that MO-HPIMF can successfully track all targets of interest and is superior to track-to-track fusion (T2TF, a commonly used approach in distributed sensor fusion system) which relies on hard association decisions.","PeriodicalId":344737,"journal":{"name":"2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129456474","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

Self-Supervised Learning and Multi-Task Pre-Training Based Single-Channel Acoustic Denoising 基于自监督学习和多任务预训练的单通道声学去噪

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913855

Yi Li, Yang Sun, S. M. Naqvi

{"title":"Self-Supervised Learning and Multi-Task Pre-Training Based Single-Channel Acoustic Denoising","authors":"Yi Li, Yang Sun, S. M. Naqvi","doi":"10.1109/MFI55806.2022.9913855","DOIUrl":"https://doi.org/10.1109/MFI55806.2022.9913855","url":null,"abstract":"In self-supervised learning-based single-channel speech denoising problem, it is challenging to reduce the gap between the denoising performance on the estimated and target speech signals with existed pre-tasks. In this paper, we propose a multi-task pre-training method to improve the speech denoising performance within self-supervised learning. In the proposed pre-training autoencoder (PAE), only a very limited set of unpaired and unseen clean speech signals are required to learn speech latent representations. Meanwhile, to solve the limitation of existing single pre-task, the proposed masking module exploits the dereverberated mask and estimated ratio mask to denoise the mixture as the new pre-task. The downstream task autoencoder (DAE) utilizes unlabeled and unseen reverberant mixtures to generate the estimated mixtures. The DAE is trained to share a latent representation with the clean examples from the learned representation in the PAE. Experimental results on a benchmark dataset demonstrate that the proposed method outperforms the state-of-the-art approaches.","PeriodicalId":344737,"journal":{"name":"2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130250025","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

UPC-Faster-RCNN: A Dynamic Self-Labeling Algorithm for Open-Set Object Detection Based on Unknown Proposal Clustering UPC-Faster-RCNN:一种基于未知建议聚类的开集目标检测动态自标记算法

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913863

Yujun Liao, Y. Wu, Y. Mo, Feilin Liu, Yufei He, Junqiao Zhao

引用次数: 0

A Study of Fusions of Multiple Estimates for Limit Cases 极限情况下多重估计的融合研究

2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Pub Date : 2022-09-20 DOI: 10.1109/MFI55806.2022.9913842

Jiří Ajgl, O. Straka

引用次数: 0