Sequential Monte Carlo Methods for Dynamic Systems

Liu, Jun S.; Chen, Rong

doi:10.2307/2669847

Cited by 1,298 publications

(1,023 citation statements)

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“…This sampling scheme is called factored sampling (Isard and Blake, 1996;Blake and Isard, 1998;Liu and Chen, 1998;Liu et al, 2000). It can be shown that such a sample set is properly weighted.…”

Section: Factored Samplingmentioning

confidence: 99%

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Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning

Huang

2004

International Journal of Computer Vision

131

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Abstract. Visual tracking can be treated as a parameter estimation problem that infers target states based on image observations from video sequences. A richer target representation would incur better chances of successful tracking in cluttered and dynamic environments, and thus enhance the robustness. Richer representations can be constructed by either specifying a detailed model of a single cue or combining a set of rough models of multiple cues. Both approaches increase the dimensionality of the state space, which results in a dramatic increase of computation. To investigate the integration of rough models from multiple cues and to explore computationally efficient algorithms, this paper formulates the problem of multiple cue integration and tracking in a probabilistic framework based on a factorized graphical model. Structured variational analysis of such a graphical model factorizes different modalities and suggests a co-inference process among these modalities. Based on the importance sampling technique, a sequential Monte Carlo algorithm is proposed to provide an efficient simulation and approximation of the co-inferencing of multiple cues. This algorithm runs in real-time at around 30Hz. Our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios. The approach presented in this paper has the potential to solve other problems including sensor fusion problems.

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Section: Factored Samplingmentioning

confidence: 99%

“…Sequential Monte Carlo methods for dynamic systems are also studied in the area of statistics (Liu and Chen, 1998;Liu et al, 2000;Doucet et al, 2000). A set of weighted random samples {(s (n) , π (n) )}, n = 1, .…”

Section: Monte Carlo Trackingmentioning

confidence: 99%

Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning

Huang

2004

International Journal of Computer Vision

131

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“…Residual Resampling is a resampling technique that can replace the simple random sampling providing favorable computation time and diminishing particles' Monte Carlo variation (Liu & Chen, 1998). It consists of the following steps:…”

Section: Residual Samplingmentioning

confidence: 99%

Modeling and Inference with Relational Dynamic Bayesian Networks

Manfredotti

2009

Lecture Notes in Computer Science

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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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“…An alternative but more computationally intensive recursive Bayesian estimation framework to the Kalman filter and its variants is the particle filter (Doucet et al 2001;Liu & Chen 1998;Pitt & Shephard 1999) which approximates the probability distribution of the estimated states as a discrete set of weighted samples. It can represent any arbitrary probability distribution, including multimodal distributions and also allows arbitrary non-linear process and measurement models, making it suitable for highly non-linear systems with non-Gaussian noise (Thrun 2002).…”

Section: Gps and Sensor Fusion -Integrated Navigationmentioning

confidence: 99%

Visually-Aided Localisation for Autonomous Agricultural Vehicles

English¹

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This thesis presents an approach to visually-aided navigation of agricultural robots in cropping fields.Many agricultural robots and vehicles navigate solely using sophisticated RTK-GPS/INS systems, however these systems remain expensive or are prone to signal occlusion. Alternatively, navigation in agricultural fields can be performed relative to crop rows using local sensors such as cameras. However, current visual crop row tracking algorithms are applicable to only a subset of fields since they make strong and restrictive assumptions about the appearance of the crop. Additionally, current navigation filters for agricultural robots do not fuse crop row tracking together with global navigation sensors to improve their position estimate.This thesis proposes a localisation system suitable for small agricultural robots that augments low-cost GPS and odometry sensors with novel visual crop row tracking methods. In working towards this goal this thesis provides two main contributions.

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Sequential Monte Carlo Methods for Dynamic Systems

Cited by 1,298 publications

References 35 publications

Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning

Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning

Modeling and Inference with Relational Dynamic Bayesian Networks

Visually-Aided Localisation for Autonomous Agricultural Vehicles

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