2008
DOI: 10.1007/978-3-540-88682-2_10
|View full text |Cite
|
Sign up to set email alerts
|

Simultaneous Motion Detection and Background Reconstruction with a Mixed-State Conditional Markov Random Field

Abstract: We consider the problem of motion detection by background subtraction. An accurate estimation of the background is only possible if we locate the moving objects; meanwhile, a correct motion detection is achieved if we have a good available background model. This work proposes a new direction in the way such problems are considered. The main idea is to formulate this class of problem as a joint decision-estimation unique step. The goal is to exploit the way two processes interact, even if they are of a dissimil… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
20
0

Year Published

2009
2009
2017
2017

Publication Types

Select...
4
2
1

Relationship

1
6

Authors

Journals

citations
Cited by 16 publications
(20 citation statements)
references
References 16 publications
0
20
0
Order By: Relevance
“…The potential of our method shown in Fig. 7.14 is satisfying when compared to the state-of-the-art background subtraction methods which can handle illumination changes and moving objects [100,41,93,72,109,33,46,79]. Our method did the same job using two input images and sometimes outperformed several methods [93,41,2,46,79] which require a long image sequence as input.…”
Section: 2mentioning
confidence: 71%
See 2 more Smart Citations
“…The potential of our method shown in Fig. 7.14 is satisfying when compared to the state-of-the-art background subtraction methods which can handle illumination changes and moving objects [100,41,93,72,109,33,46,79]. Our method did the same job using two input images and sometimes outperformed several methods [93,41,2,46,79] which require a long image sequence as input.…”
Section: 2mentioning
confidence: 71%
“…Unlike previous methods (kernelbased [41,72,92] or mixture of Gaussian-based [93,109,51]) which assume a time series of images as input, we consider the scenario where we have in our possession two images, without prior knowledge of the scene as considered in [65]. Background subtraction based on temporal information cannot be performed as described in [41,93,31,96,33] since we are using two images only.…”
Section: Main Contributionsmentioning
confidence: 99%
See 1 more Smart Citation
“…The detailed results 6 are presented in tables 1 and 2. Similar conclusions can be drawn from both datasets.…”
Section: Classification Resultsmentioning
confidence: 99%
“…In order to explicitly model texture dynamics, linear dynamical systems (LDS) have been proposed in [32]. Such stochastic models have been successfully applied in various contexts, from dynamic texture classification to motion segmentation [6] or tacking [5]. However, LDS is intrinsically limited by the first-order markov property and linearity assumption.…”
Section: Related Work and Contributionsmentioning
confidence: 99%