A revaluation of frame difference in fast and robust motion detection

Migliore, Davide; Matteucci, Matteo; Naccari, Matteo

doi:10.1145/1178782.1178815

Cited by 84 publications

(41 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1), typically extracted by background subtraction [4] or frame-by-frame difference [21]. To estimate the vehicle pose, some region-based algorithms minimize a metric, as for the edgebased case [22], while other algorithms calculate a convenient score for a set of hypothesized model poses [23,8].…”

Section: D Model-based Trackingmentioning

confidence: 99%

A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

Romanoni

Mussone

Rizzi

et al. 2015

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

Section: D Model-based Trackingmentioning

confidence: 99%

A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

Romanoni

Mussone

Rizzi

et al. 2015

Pattern Recognition Letters

Self Cite

View full text Add to dashboard Cite

“…One of these algorithm has been described in (Collins, Lipton, & Kanade, 1999), this algorithm exploits image difference between frames at time t and t − 1 and the difference between t and t − 2 to erase ghosting; it also keep in memory a background model to solve the foreground aperture problem. Another algorithm proposed as the integration of the two techniques is the one proposed in (Migliore, Matteucci, & Naccari, 2006) where an image of background is updated according to the result of the single difference on the current frame. Despite these approaches obtain good results, they spend a high computational effort to solve problems introduced by the integration of the two methods.…”

Section: Traditional Approaches To Motion Detectionmentioning

confidence: 99%

Modeling and Inference with Relational Dynamic Bayesian Networks

Manfredotti

2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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 ...

show abstract

“…The background difference method uses various algorithms to obtain the background image, and then subtracts the background The inter-frame difference method is another method of target detection that uses the difference between two or more frames to obtain the shape, position and other information about the moving object [9][10][11]. Based on the continuous difference between two or more frames to update the background information, an entire background model is obtained to extract moving objects [10]. Moreover, the moving objects in image sequences are obeyed high-order statistical distributions.…”

Section: Introductionmentioning

confidence: 99%

Detection of Moving Ships in Sequences of Remote Sensing Images

Yao

Chang

Cheng

et al. 2017

IJGI

View full text Add to dashboard Cite

High-speed agile remote sensing satellites have the ability to capture multiple sequences of images. However, the frame rate is lower and the baseline between each image is much longer than normal image sequences. As a result, the edges and shadows in each image in the sequence vary considerably. Therefore, more requirements are placed on the target detection algorithm. Aiming at the characteristics of multi-view image sequences, we propose an approach to detect moving ships on the water surface. Based on marker controlled watershed segmentation, we use the extracted foreground and background images to segment moving ships, and we obtain the complete shape and texture information of the ships. The inter-frame difference algorithm is applied to extract the foreground object information, while Otsu's algorithm is used to extract the image background. The foreground and background information is fused to solve the problem of interference with object detection caused by long imaging baseline. The experimental results show that the proposed method is effective for moving ship detection.

show abstract

A revaluation of frame difference in fast and robust motion detection

Cited by 84 publications

References 6 publications

A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

Modeling and Inference with Relational Dynamic Bayesian Networks

Detection of Moving Ships in Sequences of Remote Sensing Images

Contact Info

Product

Resources

About