Scalable Adaptive Multitarget Tracking Using Multiple Sensors

2016 ◽  
Author(s):  
Florian Meyer ◽  
Paolo Braca ◽  
Franz Hlawatsch ◽  
Michele Micheli ◽  
Kevin D. LePage
Keyword(s):  
Multitarget Tracking ◽  
Multiple Sensors

scholarly journals Tracking Multiple Marine Ships via Multiple Sensors with Unknown Backgrounds

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5025 ◽  
Author(s):  
Cong-Thanh Do ◽  
Tran Thien Dat Nguyen ◽  
Weifeng Liu
Keyword(s):  
State Of The Art ◽  
Numerical Study ◽  
Probability Of Detection ◽  
Multitarget Tracking ◽  
Time Varying ◽  
Multiple Sensors ◽  
Doppler Data ◽  
Online Tracking ◽  
Bernoulli Filter ◽  
Parallel Estimation

In multitarget tracking, knowledge of the backgrounds plays a crucial role in the accuracy of the tracker. Clutter and detection probability are the two essential background parameters which are usually assumed to be known constants although they are, in fact, unknown and time varying. Incorrect values of these parameters lead to a degraded or biased performance of the tracking algorithms. This paper proposes a method for online tracking multiple targets using multiple sensors which jointly adapts to the unknown clutter rate and the probability of detection. An effective filter is developed from parallel estimation of these parameters and then feeding them into the state-of-the-art generalized labeled multi-Bernoulli filter. Provided that the fluctuation of these unknown backgrounds is slowly-varying in comparison to the rate of measurement-update data, the validity of the proposed method is demonstrated via numerical study using multistatic Doppler data.

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors

A Parallel Retrodiction Algorithm for Large-Scale Multitarget Tracking

2020 ◽  
pp. 1-1
Author(s):  
Siu Lun Yeung ◽  
Sean Tager ◽  
Paul Wilson ◽  
Ratnasingham Tharmarasa ◽  
Wes Armour ◽  
...  
Keyword(s):  
Large Scale ◽  
Multitarget Tracking

scholarly journals Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2146
Author(s):  
Manuel Andrés Vélez-Guerrero ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Main Trend ◽  
Motor Rehabilitation ◽  
Rehabilitation Process ◽  
Upper Limb Rehabilitation ◽  
Multiple Sensors ◽  
Meta Analyses ◽  
Limb Rehabilitation ◽  
And Control

Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

scholarly journals Consensus-based Distributed Algorithm for Multisensor-Multitarget Tracking under Unknown–but–Bounded Disturbances

2020 ◽  
Vol 53 (2) ◽  
pp. 3589-3595
Author(s):  
Natalia Amelina ◽  
Victoria Erofeeva ◽  
Oleg Granichin ◽  
Yury Ivanskiy ◽  
Yuming Jiang ◽  
...  
Keyword(s):  
Distributed Algorithm ◽  
Multitarget Tracking ◽  
Bounded Disturbances

scholarly journals Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

2021 ◽  
Vol 11 (2) ◽  
pp. 582
Author(s):  
Zean Bu ◽  
Changku Sun ◽  
Peng Wang ◽  
Hang Dong
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Calibration Method ◽  
Multiple Sensors ◽  
Triangular Pyramid ◽  
World Coordinate System ◽  
Flash Lidar ◽  
Novel Method ◽  
3D Information ◽  
Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

scholarly journals Perylene Imide-Based Optical Chemosensors for Vapor Detection

Chemosensors ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Miao Zhang ◽  
Jiangfan Shi ◽  
Chenglong Liao ◽  
Qingyun Tian ◽  
Chuanyi Wang ◽  
...  
Keyword(s):  
Molecular Design ◽  
Phase Detection ◽  
Time Reversibility ◽  
Strong Electron ◽  
Vapor Detection ◽  
Multiple Sensors ◽  
Optical Chemosensors ◽  
Potential Applications ◽  
High Fluorescence ◽  
Organic Amines

Perylene imide (PI) molecules and materials have been extensively studied for optical chemical sensors, particularly those based on fluorescence and colorimetric mode, taking advantage of the unique features of PIs such as structure tunability, good thermal, optical and chemical stability, strong electron affinity, strong visible light absorption and high fluorescence quantum yield. PI-based optical chemosensors have now found broad applications in gas phase detection of chemicals, including explosives, biomarkers of some food and diseases (such as organic amines (alkylamines and aromatic amines)), benzene homologs, organic peroxides, phenols and nitroaromatics, etc. In this review, the recent research on PI-based fluorometric and colorimetric sensors, as well as array technology incorporating multiple sensors, is reviewed along with the discussion of potential applications in environment, health and public safety areas. Specifically, we discuss the molecular design and aggregate architecture of PIs in correlation with the corresponding sensor performances (including sensitivity, selectivity, response time, recovery time, reversibility, etc.). We also provide a perspective summary highlighting the great potential for future development of PIs optical chemosensors, especially in the sensor array format that will largely enhance the detection specificity in complexed environments.

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