Non-parametric Model for Background Subtraction

Elgammal, Ahmed; Harwood, David; Davis, Larry S.

doi:10.1007/3-540-45053-x_48

Cited by 1,587 publications

(1,290 citation statements)

References 2 publications

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“…They represent the background distribution with a set of samples drawn from this distribution. This is the case for the KDE [4] and ViBe [1] algorithms. In this study, we assume that the model is an estimated background image (this is the topic of the SBMI workshop [11]).…”

Section: The Traditional Processing Pipeline Of Bgs Algorithmsmentioning

confidence: 85%

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Pierard

Droogenbroeck

2015

New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops

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Abstract. The quest for the best background subtraction technique is ongoing. Despite that a considerable e ort has been undertaken to develop exible and e cient methods, some elementary questions are still unanswered. One of them is the existence of an intrinsic upper bound to the performance. In fact, data are a ected by noise, and therefore it is illusory to believe that it is possible to achieve a perfect segmentation.This paper aims at exploring some intrinsic limitations of the principle of background subtraction. The purpose consists in studying the impact of several limiting factors separately. One of our conclusions is that even if an algorithm would be able to calculate a perfect background image, it is not su cient to achieve a perfect segmentation with background subtraction, due to other intrinsic limitations.

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Section: The Traditional Processing Pipeline Of Bgs Algorithmsmentioning

confidence: 85%

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Pierard

Droogenbroeck

2015

New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops

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“…To filter out non-fire moving pixels, we compare their values with a predefined RGB colour distribution created by a number of pixel-samples from video sequences containing real fires. The probability density function of a moving pixel is non-parametrically estimated, according to the technique proposed in [15]. After the blob analysis step, the colour probability of each candidate blob is estimated by summing the colour probabilities of all pixels in the blob.…”

Section: Flame Detection Combining Multiple Features and Svm Or Ruledmentioning

confidence: 99%

Flame Detection for Video-Based Early Fire Warning for the Protection of Cultural Heritage

Dimitropoulos

Günay

Kose

et al. 2012

Progress in Cultural Heritage Preservation

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Abstract. Cultural heritage and archaeological sites are exposed to the risk of fire and early warning is the only way to avoid losses and damages. The use of terrestrial systems, typically based on video cameras, is currently the most promising solution for advanced automatic wildfire surveillance and monitoring. Video cameras are sensitive in visible spectra and can be used either for flame or smoke detection. This paper presents and compares three video-based flame detection techniques, which were developed within the FIRESENSE EU research project.

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“…As an example of non-parametric approaches to background modeling, consider the model presented in (Elgammal, Harwood, & Davis, 2000) where the density function of the distribution of each pixel in the scene is estimated at any moment of time given only very recent (n frames) history information. With the median filter approach, proposed in (R. Cutler & L.Davis, 1998) and in (R. Cucchiara & A.Prati, 2003), one computes each pixel of the background image as the average of the corresponding pixels in the n previous images.…”

Section: Traditional Approaches To Motion Detectionmentioning

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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Non-parametric Model for Background Subtraction

Cited by 1,587 publications

References 2 publications

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

A Perfect Estimation of a Background Image Does Not Lead to a Perfect Background Subtraction: Analysis of the Upper Bound on the Performance

Flame Detection for Video-Based Early Fire Warning for the Protection of Cultural Heritage

Modeling and Inference with Relational Dynamic Bayesian Networks

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