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Modelling and Simulation of Multi-target Multi-sensor Data Fusion for Trajectory Tracking
English

A. Singh, N. Sood
<span title="2009-05-25">2009</span> <i title="Defence Scientific Information and Documentation Centre"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sbz4qubtlrhx5a2wpmby2l2qfu" style="color: black;">Defence Science Journal</a> </i> &nbsp;
The joint probabilistic data association filter (JPDAF) and neural network fusion (NNF) algorithms were used for tracking multiple man-euvring targets.  ...  Track association and fusion algorithm are executed to get the fused track data for various scenarios, two sensors tracking a single target to three sensors tracking three targets, to evaluate the effects  ...  ACKNOWLADGEMENTS Authors are thankful to Prof G C Pant, Chairman, Department of Aerospace Engineering, Defence Institute of Advanced Technology(DIAT), Girinagar, Pune, for the encouragement and also to  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14429/dsj.59.1513">doi:10.14429/dsj.59.1513</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5h5yby7yebhvhnal7uafj6fbcu">fatcat:5h5yby7yebhvhnal7uafj6fbcu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030043638/http://publications.drdo.gov.in/ojs/index.php/dsj/article/download/1513/644" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/a7/c3a73c57804d611c06be75cbecbbf91cd506d615.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14429/dsj.59.1513"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Modular Multi Target Tracking Using LSTM Networks [article]

Rishabh Verma, R Rajesh, MS Easwaran
<span title="2020-11-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper proposes a model free end-to-end approach for airborne target tracking system using sensor measurements, integrating all the key elements of multi target tracking -- association, prediction  ...  It is shown that performance of the proposed technique outperforms conventional state of the art technique Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) filter.  ...  The above models are used for the generation of truth and measurements for training deep neural networks. Fig. 1 Multi Target Tracker III.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.09839v1">arXiv:2011.09839v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/je24h5pdyrebfov2ys7thffbua">fatcat:je24h5pdyrebfov2ys7thffbua</a> </span>
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Filtering Point Targets via Online Learning of Motion Models [article]

Mehryar Emambakhsh, Alessandro Bay, Eduard Vazquez
<span title="2019-02-20">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address these issues, this paper proposes a multi-target filtering algorithm which learns the motion models, on the fly, using a recurrent neural network with a long short-term memory architecture,  ...  Filtering point targets in highly cluttered and noisy data frames can be very challenging, especially for complex target motions.  ...  Schindler, “Online measures and a data set for multi-target, multi-camera tracking,” in multi-target tracking using recurrent neural networks,” in AAAI, 2017.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.07630v1">arXiv:1902.07630v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/emcostdvibfnjjg23lbljwkjgm">fatcat:emcostdvibfnjjg23lbljwkjgm</a> </span>
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Online Multi-Object Tracking Based on Feature Representation and Bayesian Filtering within a Deep Learning Architecture

Jun Xiang, Guoshuai Zhang, Jianhua Hou
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In detection-based multi-object tracking (MOT), one challenging problem is to design a robust affinity model for data association.  ...  We design a recurrent neural network-based Bayesian filtering module, which takes a hidden state of the above-mentioned LSTM network as an input and performs recursive prediction and update for explicitly  ...  Finally, Bayesian filtering module(BF-Net) embedded in M-Net is employed to refine tracks with or without the associated detections. data association and how to design Bayesian filtering network for trajectory  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2901520">doi:10.1109/access.2019.2901520</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yabmic6svnbx5ljbfaaeubt4zi">fatcat:yabmic6svnbx5ljbfaaeubt4zi</a> </span>
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Analysis Based on Recent Deep Learning Approaches Applied in Real-Time Multi-Object Tracking: A Review

Lesole Kalake, Wanggen Wan, Li Hou
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Target Uncertainty in Online MOT, and Deep Convolutional Neural Network (DCNN), Affinity and Data Association.  ...  The strength of the technique's features has enabled researchers to analyze object trajectories across multiple cameras for online multi-object tracking (MOT) systems.  ...  They are designed in an end-to-end deep neural network to learn the association between tracks and detections, statement updates, initialization, and termination of tracks [40] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3060821">doi:10.1109/access.2021.3060821</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ummv6sm4czhgjfffdbwx5lomfm">fatcat:ummv6sm4czhgjfffdbwx5lomfm</a> </span>
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Deep Reinforcement Learning for Data Association in Cell Tracking

Junjie Wang, Xiaohong Su, Lingling Zhao, Jun Zhang
<span title="2020-04-09">2020</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tkuhrcyiufdxtkdmjqvay6f2ua" style="color: black;">Frontiers in Bioengineering and Biotechnology</a> </i> &nbsp;
Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells.  ...  According to the dynamic model of each target, the cost matrix is produced by conjointly considering various features of targets and then used as the input of a neural network.  ...  AUTHOR CONTRIBUTIONS LZ and JW substantially contributed to the conception and design of the study. XS analyzed and interpreted the data. LZ, JW, and JZ drafted the article.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fbioe.2020.00298">doi:10.3389/fbioe.2020.00298</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32328484">pmid:32328484</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7161216/">pmcid:PMC7161216</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cy5pxbroa5fhrokzvaereycnpm">fatcat:cy5pxbroa5fhrokzvaereycnpm</a> </span>
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Deep Recurrent Neural Network for Multi-target Filtering [article]

Mehryar Emambakhsh, Alessandro Bay, Eduard Vazquez
<span title="2018-10-08">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper addresses the problem of fixed motion and measurement models for multi-target filtering using an adaptive learning framework.  ...  A novel data association algorithm compatible with the predicted tracklet tuples is proposed, enabling the update of occluded targets, in addition to assigning birth, survival and death of targets.  ...  Particularly for a tracking problem, the performance of a multi-target tracking algorithm heavily relies on its filtering step.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1806.06594v2">arXiv:1806.06594v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zzh2ocg4cvhjbb352wy37xjkr4">fatcat:zzh2ocg4cvhjbb352wy37xjkr4</a> </span>
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Multi-target tracking for video surveillance using deep affinity network: a brief review [article]

Sanam Nisar Mangi
<span title="2021-10-29">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Multi-target tracking (MTT) for video surveillance is one of the important and challenging tasks, which has attracted the researcher's attention due to its potential applications in various domains.  ...  Multi-target tracking tasks require locating the objects individually in each frame, which remains a huge challenge as there are immediate changes in appearances and extreme occlusions of objects.  ...  [44] proposed a unique approach for multi-person tracking by-detection in a particle filtering framework, employing an online trained classifier for data association, they investigated how unreliable  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.15674v1">arXiv:2110.15674v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oungod3r4rdhdf3lcemuxx7aqu">fatcat:oungod3r4rdhdf3lcemuxx7aqu</a> </span>
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Papertitles

<span title="">2019</span> <i title="IEEE"> 2019 International Conference on Control, Automation and Information Sciences (ICCAIS) </i> &nbsp;
P R S T V Nearest-neighbour Joint Probabilistic Data Association Filter Based on Random Finite Set Neural Q-Learning Based on Residual Gradient for Nonlinear Control Systems NLOS Targets Imaging with  ...  in dense clutter environment Multi-Robot Cooperative SLAM Based on Distributed Extended Kalman Filter Multi-sensor Multi-target Tracking Using Labelled Random Finite Sets with Homography Data Multi-Target  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccais46528.2019.9074559">doi:10.1109/iccais46528.2019.9074559</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/srkln5llk5czjgcksdtuhlgwdy">fatcat:srkln5llk5czjgcksdtuhlgwdy</a> </span>
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Authors List

<span title="">2020</span> <i title="IEEE"> 2020 National Conference on Communications (NCC) </i> &nbsp;
Gamma Band Brejesh Lall Data Adaptive Compressed Sensing Using Deep Neural Network for Image Recognition Aerial Multi-Object Tracking by Detection Using Deep Association Networks Blind Channel  ...  Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data Rabul Laskar Semantic Segmentation Based Hand Gesture Recognition Using Deep Neural Networks  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ncc48643.2020.9056032">doi:10.1109/ncc48643.2020.9056032</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tsdhbqblujfwlgf4ojr6ftqdhe">fatcat:tsdhbqblujfwlgf4ojr6ftqdhe</a> </span>
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Multiple Ship Tracking in Remote Sensing Images Using Deep Learning

Jin Wu, Changqing Cao, Yuedong Zhou, Xiaodong Zeng, Zhejun Feng, Qifan Wu, Ziqiang Huang
<span title="2021-09-09">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object tracking (MOT) algorithm for ships using deep learning was proposed in this study.  ...  The high similarity of ship targets will cause poor tracking results; therefore, we used the multiple granularity network (MGN) to extract richer target appearance information to improve the generalization  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs13183601">doi:10.3390/rs13183601</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xq4ulikh7zfznliwh6zbssiqw4">fatcat:xq4ulikh7zfznliwh6zbssiqw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210915012007/https://mdpi-res.com/d_attachment/remotesensing/remotesensing-13-03601/article_deploy/remotesensing-13-03601.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/05/ad/05ad05c103c52681138fc4ef00b60faf9bfbaa2e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs13183601"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Online Multi-Target Tracking Using Recurrent Neural Networks [article]

Anton Milan, Seyed Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler
<span title="2016-12-07">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present a novel approach to online multi-target tracking based on recurrent neural networks (RNNs).  ...  c) a discrete combinatorial problem of data association.  ...  This work was supported by ARC Linkage Project LP130100154, ARC Laureate Fellowship FL130100102 and the ARC Centre of Excellence for Robotic Vision CE140100016.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1604.03635v2">arXiv:1604.03635v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p234duid4jeqnh2to3ubl2vlyq">fatcat:p234duid4jeqnh2to3ubl2vlyq</a> </span>
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A Review of Computer Vision Technologies for Fish Tracking [article]

Zhenbo Li, Weiran Li, Fei Li, Meng Yuan
<span title="2022-01-21">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Most of the applications of fish tracking use classic filtering algorithms, which lack in accuracy and efficiency.  ...  To solve this issue, deep learning methods utilized deep neural networks to extract the features, which achieve a good performance in the fish tracking.  ...  They used the GNN to deal with the problem of data association of online multi-target tracking.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.02551v3">arXiv:2110.02551v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kr6uqjmj6zgtnaojnuyumdvwva">fatcat:kr6uqjmj6zgtnaojnuyumdvwva</a> </span>
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Table of contents

<span title="">2018</span> <i title="IEEE"> 2018 14th IEEE International Conference on Signal Processing (ICSP) </i> &nbsp;
BASED ON DEEP NEURAL NETWORK: DILATED DENSENET OF TWO LEVEL LOSSES Xingrong Zhao; Zheng Liu; Heming Zhao; Xueqin Chen 355 TARGET RE-DETECTION AND TRACKING BASED ON CORRELATED FILTER Yuqiu Wu  ...  859 AMPLITUDE INFORMATION AIDED ROBUST MULTI-BERNOULLI FILTER FOR MARINE TARGET TRACKING Chao Liu; Zhiguo Zhang; Jinping Sun; Yaolong Qi CRAMéR RAO LOWER BOUND DERIVATION OF PARAMETER ESTIMATION FOR HYPERSONIC  ... 
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A Data Fusion Methodology for Wireless Sensor Systems

Joy Iong-Zong Chen, Yi-Nung Chung
<span title="2012-03-01">2012</span> <i title="Agora University of Oradea"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/do4a7az725gcpbh4iwwih3j7aa" style="color: black;">International Journal of Computers Communications &amp; Control</a> </i> &nbsp;
specified simulated targets can also be tracked without large scale missing. data association consists of the three aforementioned steps.  ...  A DFA based on CHHN (competitive Hopfield neural network) technique for obtaining the relationship between measurements results from wireless mobile sensors and estimation of existing tracks over WSS (  ...  The authors also would like to thank the anonymous reviewers for many useful comments which help to improve the quality and readability of this paper.  ... 
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