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Relevant bibliographies by topics / Multiple target tracking algorithms

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Academic literature on the topic 'Multiple target tracking algorithms'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Multiple target tracking algorithms"

1

Wu, Jin, Changqing Cao, Yuedong Zhou, et al. "Multiple Ship Tracking in Remote Sensing Images Using Deep Learning." Remote Sensing 13, no. 18 (2021): 3601. http://dx.doi.org/10.3390/rs13183601.

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In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. 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 feature extraction capability of target detectors determines the performance of MOT algorithms. Therefore, you only look once (YOLO)-v3 model, which has better accuracy and speed than other algorithms, was selected as the target detection framework. The high similari

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Ding, Ma. "Tracking Target Identification Model Based on Multiple Algorithms." Applied Mechanics and Materials 539 (July 2014): 106–12. http://dx.doi.org/10.4028/www.scientific.net/amm.539.106.

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In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish ta

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Ling, Jiankun. "Target Tracking Using Kalman Filter Based Algorithms." Journal of Physics: Conference Series 2078, no. 1 (2021): 012020. http://dx.doi.org/10.1088/1742-6596/2078/1/012020.

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Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simul

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Hoang, Le Minh, Aleksandr A. Konovalov, and Dao Van Luc. "Tracking of Maneuvering Targets Using a Variable Structure Multiple Model Algorithm." Journal of the Russian Universities. Radioelectronics 26, no. 3 (2023): 77–89. http://dx.doi.org/10.32603/1993-8985-2023-26-3-77-89.

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Introduction. In recent years, much attention has been paid to the development of trajectory filtering methods for tracking maneuvering targets. Multi-model (MM) algorithms are widely used for filtering maneuvering targets. Conventional MM algorithms are characterized by a fixed structure. However, highly maneuvering targets require a sufficiently large set of models covering the entire range of possible maneuvers, although an increase in the number of models cannot ensure an increase in the accuracy of tracking. To overcome these problems, multiple model algorithms with a variable structure (

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Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.

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Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Fi

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Lei Shundong. "Tracking Target Identification Model Based on Multiple Algorithms." International Journal of Digital Content Technology and its Applications 7, no. 3 (2013): 274–83. http://dx.doi.org/10.4156/jdcta.vol7.issue3.35.

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Memon, Sufyan, Myungun Kim, and Hungsun Son. "Tracking and Estimation of Multiple Cross-Over Targets in Clutter." Sensors 19, no. 3 (2019): 741. http://dx.doi.org/10.3390/s19030741.

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Tracking problems, including unknown number of targets, target trajectories behaviour and uncertain motion of targets in the surveillance region, are challenging issues. It is also difficult to estimate cross-over targets in heavy clutter density environment. In addition, tracking algorithms including smoothers which use measurements from upcoming scans to estimate the targets are often unsuccessful in tracking due to low detection probabilities. For efficient and better tracking performance, the smoother must rely on backward tracking to fetch measurement from future scans to estimate forward

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Memon, Sufyan Ali, Hungsun Son, Wan-Gu Kim, Abdul Manan Khan, Mohsin Shahzad, and Uzair Khan. "Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace." Drones 7, no. 4 (2023): 241. http://dx.doi.org/10.3390/drones7040241.

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In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid t

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Chen, Yuntao, Bin Wu, guangzhi Luo, xiaoyan Chen, and junlin Liu. "Multi-target tracking algorithm based on YOLO+DeepSORT." Journal of Physics: Conference Series 2414, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1742-6596/2414/1/012018.

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Abstract After several years of development, the multi-target tracking algorithm has significantly transitioned from being researched to being put into practical production and life. The application field of human detection and tracking technology is closely related to our daily life. In order to solve the problems of the background complexity, the diversity of object shapes in the application of multi-target algorithms, and the mutual occlusion between multiple tracking targets and the lost target, this paper improves the DeepSORT target tracking algorithm, uses the improved YOLO network to d

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Song, Xiyu, Nae Zheng, and Ting Bai. "Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems." International Journal of Antennas and Propagation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7241281.

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Considering the demands of different location accuracy for multiple targets tracking, performance-driven resource allocation schemes in distributed MIMO radar system are proposed. Restricted by the tracking antenna number, location estimation mean-square error (MSE), and target priorities, an optimization problem of the minimal antenna subsets selection is modeled as a knapsack problem. Then, two operational schemes, modified fair multistart local search (MFMLS) algorithm and modified fair multistart local search with one antenna to all targets (MFMLS_OAT) algorithm, are presented and evaluate

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Dissertations / Theses on the topic "Multiple target tracking algorithms"

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Pitre, Ryan. "A Comparison of Multiple-Model Target Tracking Algorithms." ScholarWorks@UNO, 2004. http://louisdl.louislibraries.org/u?/NOD,168.

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Thesis (M.S.)--University of New Orleans, 2004.<br>Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical Engineering."--Thesis t.p. Vita. Includes bibliographical references.

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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, i

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Naeem, Asad. "Single and multiple target tracking via hybrid mean shift/particle filter algorithms." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/12699/.

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This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of

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Hadzagic, Melita. "Comparative analysis of the IMM-JVC and the IMM-JPDA algorithms for multiple-target tracking." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32959.

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When tracking closely maneuvering targets, the critical role is played by both the chosen method of data association and the target-tracking algorithm. Without an effective association, state estimation is at risk. Without an efficient state prediction, the performance of an associator can be degraded. In developing an assignment strategy the crucial issue is whether to assign a track or observation as belonging uniquely to another observation or track, or to allow a track to be associated non-uniquely with multiple candidate observations.<br>This thesis presents a comparative study of two ass

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Munir, Arshed. "Manoeuvring target tracking using different forms of the interacting multiple model algorithm." Thesis, University of Sussex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240430.

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Alat, Gokcen. "A Variable Structure - Autonomous - Interacting Multiple Model Ground Target Tracking Algorithm In Dense Clutter." Phd thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615512/index.pdf.

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Tracking of a single ground target using GMTI radar detections is considered. A Variable Structure- Autonomous- Interactive Multiple Model (VS-A-IMM) structure is developed to address challenges of ground target tracking, while maintaining an acceptable level computational complexity at the same time. The following approach is used in this thesis: Use simple tracker structures<br>incorporate a priori information such as topographic constraints, road maps as much as possible<br>use enhanced gating techniques to minimize the eect of clutter<br>develop methods against stop-move motion and hide mo

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Ege, Emre. "A Comparative Study Of Tracking Algorithms In Underwater Environment Using Sonar Simulation." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608866/index.pdf.

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Target tracking is one the most fundamental elements of a radar system. The aim of target tracking is the reliable estimation of a target&#039<br>s true state based on a time history of noisy sensor observations. In real life, the sensor data may include substantial noise. This noise can render the raw sensor data unsuitable to be used directly. Instead, we must filter the noise, preferably in an optimal manner. For land, air and surface marine vehicles, very successful filtering methods are developed. However, because of the significant differences in the underwater propagation environment an

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Niedfeldt, Peter C. "Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4195.

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Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poo

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Day, Nathalie Anna. "Significant measurements of a multiple target tracking system utilizing munkre's algorithm as a correlation scheme." Master's thesis, University of Central Florida, 1988. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/72470.

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University of Central Florida College of Engineering Thesis<br>This thesis presents and discusses the principles of multiple target tracking. A simulation written in Turbo Pascal provides the results of using a modified version of Munkre's algorithm for correlating targets with observations. The number and types of measurments necessary to obtain acceptable results are examined. The measurements under scrutiny are range, range rate, azimuth angle and elevation angle. A track-while-scan system is assumed and the nearest neighbor correlation scheme as well as rectangular gating are used for

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Sahin, Mehmet Alper. "Performance Optimization Of Monopulse Tracking Radar." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605364/index.pdf.

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An analysis and simulation tool is developed for optimizing system parameters of the monopulse target tracking radar and observing effects of the system parameters on the performance of the system over different scenarios. A monopulse tracking radar is modeled for measuring the performance of the radar with given parameters, during the thesis studies. The radar model simulates the operation of a Class IA type monopulse automatic tracking radar, which uses a planar phased array. The interacting multiple model (IMM) estimator with the Probabilistic Data Association (PDA) technique is used as the

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Books on the topic "Multiple target tracking algorithms"

1

Multiple-target tracking with radar applications. Artech House, 1986.

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Nassimizadeh, Hamid. Data association and multiple target tracking. University of Birmingham, 1992.

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Dunham, Darin T. Tracking multiple targets in cluttered environments with the probabilistic multi-hypothesis tracking filter. Naval Postgraduate School, 1997.

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IEE Seminar on Target Tracking: Algorithms and Applications (2006 Birmingham, England). The IEE seminar on target tracking: algorithms and applications: 7-8 March 2006. Institution of Electrical Engineers, 2006.

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Engineers, Institution of Electrical, and IEE Control & Automation Professional Network., eds. Target tracking 2004: Algorithms and applications, 23-24 March 2004, the University of Sussex, Brighton, UK. Institution of Electrical Engineers, 2004.

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IEE Professional Network on Concepts for Automation & Control. International seminar: Target tracking, algorithms & applications : Tuesday, 16 October-Wednesday, 17 October 2001 : Conferentiehotel Drienerburght, University of Twente, Enschede, The Netherlands. Thales, 2001.

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Nicklas, Richard B. An application of a Kalman Filter Fixed Interval Smoothing Algorithm to underwater target tracking. Naval Postgraduate School, 1989.

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Dubanov, Aleksandr. Computer simulation in pursuit problems. Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena. In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out. The main attention is paid to the methods of pursuit and parallel rendezvous. The

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Bayesian Multiple Target Tracking. Artech House Publishers, 2014.

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Stone, Lawrence D., Carl A. Barlow, and Thomas L. Corwin. Bayesian Multiple Target Tracking (Artech House Radar Library). Artech House Publishers, 1999.

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Book chapters on the topic "Multiple target tracking algorithms"

1

Liu, Weifeng, Zhong Chai, and Chenglin Wen. "A Multiple Shape-Target Tracking Algorithm by Using MCMC Sampling." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31020-1_67.

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Song, Liping, and Hongbing Ji. "Least Squares Interacting Multiple Model Algorithm for Passive Multi-sensor Maneuvering Target Tracking." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_66.

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Hoh, Baik, and Marco Gruteser. "Multiple Target Tracking." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_850.

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Hoh, Baik, and Marco Gruteser. "Multiple Target Tracking." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_850-2.

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Phalke, Kiran, and Ravindra Hegadi. "Multiple Target Tracking." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8633-5_58.

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Hoh, Baik, and Marco Gruteser. "Multiple Target Tracking." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_850.

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Streit, Roy L. "Multiple Target Tracking." In Poisson Point Processes. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6923-1_6.

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Kyriakides, Ioannis, Darryl Morrell, and Antonia Papandreou-Suppappola. "Multiple Target Tracking." In Adaptive High-Resolution Sensor Waveform Design for Tracking. Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01515-1_5.

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Wu, Weihua, Hemin Sun, Mao Zheng, and Weiping Huang. "Single Target Tracking Algorithms." In Target Tracking with Random Finite Sets. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9815-7_2.

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Wang, Xiaoyu, Gang Hua, and Tony X. Han. "Discriminative Multiple Target Tracking." In Machine Learning for Vision-Based Motion Analysis. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-057-1_6.

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Conference papers on the topic "Multiple target tracking algorithms"

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Noyes, S. P. "Control of false track rate using multiple hypothesis confirmation." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040062.

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Arj, M. "Problems of multiple-target tracking in vision-based applications." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040064.

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Hue, C. "Tracking multiple targets with particle filtering using multiple receivers." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010232.

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Allam, S. "Multiple model tracking with intermittent mode observations." In IEE Colloquium. Target Tracking: Algorithms and Applications. IEE, 1999. http://dx.doi.org/10.1049/ic:19990511.

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Vahdati-khajeh, E. "Tracking the maneuvering targets using multiple scan joint probabilistic data association algorithm." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040049.

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Karlsson, R. "Monte Carlo data association for multiple target tracking." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010239.

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Clark, D., I. T. Ruiz, Y. Petillot, and J. Bell. "Multiple target tracking and data association in sonar images." In IEE Seminar on Target Tracking: Algorithms and Applications. IEE, 2006. http://dx.doi.org/10.1049/ic:20060567.

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Judge, I. "RADIX - a solution to multiple sensor data fusion." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010231.

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Boers, Y. "Multiple model filters for systems with possibly erroneous measurements." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010236.

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Jaward, M. H., L. Mihaylova, N. Canagarajah, and D. Bull. "A data association algorithm for multiple object tracking in video sequences." In IEE Seminar on Target Tracking: Algorithms and Applications. IEE, 2006. http://dx.doi.org/10.1049/ic:20060565.

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Reports on the topic "Multiple target tracking algorithms"

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Bose, N. K. Multiple Target Tracking: Fast Algorithm for Data Association and State Estimation. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada300870.

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Kashyap, Rangasami L. Multiple Target Detection and Tracking. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada363925.

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Lambert, Hendrick C., and Dana Sinno. Bioinspired Resource Management for Multiple-Sensor Target Tracking Systems. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada544935.

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Kamalvand, Ahmad, Paul MacDonald, and Thai-Duong Tran. Factored Sampling Tracking: Comparison of the Kalman and the Condensation Algorithms for Missile Tracking in a Defense Target Environment. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430271.

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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and

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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, s

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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider diseas

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