Sonar tracking of multiple targets using joint probabilistic data association

Fortmann, T.; Bar‐Shalom, Yaakov; Scheffe, M.

doi:10.1109/joe.1983.1145560

Cited by 1,165 publications

(590 citation statements)

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“…The best-known one is the joint probabilistic data association (JPDA) filter [52,53]. JPDA is a suboptimal single-scan approximation to the optimal Bayesian filter; it can also be viewed as an assumed density filter in which the joint posterior distribution is approximated by a product of simpler distributions such as moment matching Gaussian distributions.…”

Section: Bayesian Approachesmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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Section: Bayesian Approachesmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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“…The algorithm was proposed by Fortmann et al (1983) based on the probabilistic data association concept introduced by . Unlike the MHT algorithm, JPDAF does not suffer from the combinatorial explosion.…”

Section: Jpdaf Algorithmmentioning

confidence: 99%

Awareness of Road Scene Participants for Autonomous Driving

Petrovskaya

Perrollaz

Oliveira

et al. 2012

Handbook of Intelligent Vehicles

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This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model based approach, which performs inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.

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“…The multiple hypothesis tracker (Reid, 1979) and the joint probabilistic data association filter (Fortmann, Bar-Shalom, & Scheffe, 1983) are the most influential algorithms in this class. These multitarget tracking algorithms have been used extensively in the context of computer vision.…”

Section: Multi-target Trackingmentioning

confidence: 99%

Modeling and Inference with Relational Dynamic Bayesian Networks

Manfredotti

2009

Advances in Artificial Intelligence

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AcknowledgementsPrima di partire per un lungo viaggio devi portare con te la voglia di non tornare piu'.Irene Grandi from the song "Prima di partire per un lungo viaggio".Molti (maligni!) penseranno che la citazione si riferisce al mio non voler mai piu' tornare in Bicocca ... Ebbene no! Il dottorato e' stato un viaggio e come e' giusto, in quanto viaggio, l'ho percorso "gustandomelo".Il dottorato e' stata un'esperienza molto costruttiva e la crescita personale e di ricercatore che ne ho derivato non e' stata tutta merito mio ma, soprattutto, delle persone che mi hanno indirizzato e accompagnato in questo cammino e che ora mi piacerebbe ringraziare una per una.Prima tra tutte la mia relatrice la professoressa Messina ("La Prof"). Ha saputo consigliarmi sulla strada da prendere e, quando mi perdevo, ha saputo sempre guidarmi verso il raggiungimento dei miei obiettivi. La collaborazione con lei e' stata un'importante occasione di crescita per me."A braccetto" con La Prof non puo' mancare il professor Archetti. Sempre presente anche se spesso non fisicamente. Tante delle idee presenti in questa tesi sono nate dalla discussione con lui, da sue proposte e dalla sua voglia di "provare".Nel professor Sorrenti ho sempre trovato un confidente e un amico, ma anche una persona sul cui giudizio potevo contare e della cui opinione potevo fidarmi. Grazie! I tre anni di dottorato sono stati tre anni pieni di amici incontrati lungo questo cammino: Dario, Yuri (che ha consegnato la mia domanda di tesi, grazie!) Simone, Sergio, i due Daniele (che poi ho ritrovato a Toronto), Sara (Un amico e' per sempre!), Elena, Daniele (che per primo mi ha spiegato particle filter), Axel (che ora iniziera' questo "viaggio"), Paolo, Davide e tanti altri con i quali ho condiviso tanti momenti di scherzi, di pausa, di chiacchera e anche a volte di ..., grazie a tutti, ragazzi! Ma piu' di tutti devo ringraziare le mie due colleghe e compagne di (s)ventura: Elisabetta e Ilaria. Ne abbiamo passate tante insieme, lavorativamente parlando e non solo, ma i miei ringraziamenti sono soprattutto per questo ultimo anno e mezzo in cui, se pur lontana, mi avete sempre e comunque fatto sentire una di voi. Grazie! Il MIND (ex LIFE) e' stato un bellissimo gruppo nel quale lavorare (e per ognuno degli studenti passati di li' conservo un ricordo particolare) ma c'e' un altro gruppo che mi ha accolta e fatta sentire a casa in quest ultimo periodo: COGS alla UofT. Per farmi capire anche da loro cambio lingua per un attimo. IVAt UofT I found a great professor, Craig, who always gave me the right advice about the right thing to do and, more importantly, who introduced me to David, whose ideas and suggestions (during our long meetings) arrived at the right moment, a turning point for this work.But I also found a lot of colleagues and friends always there when I need something. I remember the long lunches with Laurent, that made me feeling like at home, the small talks with Kevin who supported me a lot, the "strange" conversations with Tyler and the support of Mark and ...

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Sonar tracking of multiple targets using joint probabilistic data association

Cited by 1,165 publications

References 10 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Awareness of Road Scene Participants for Autonomous Driving

Modeling and Inference with Relational Dynamic Bayesian Networks

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