Video Frame’s Background Modeling: Reviewing the Techniques

Hassanpour, Hamid; Sedighi, Mehdi; Manashty, Ali Reza

doi:10.4236/jsip.2011.22010

Cited by 33 publications

(20 citation statements)

References 5 publications

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“…Collecting results (11), (12) and (13) gives formula (10). The right hand side of (10) coincides with the value of the cumulative distribution function (CDF) of a (centered) chisquared distribution with n degrees of freedom, χ…”

Section: Results 2 For the Normal Distributionmentioning

confidence: 99%

“…Among the multiple engineering areas where the aforementioned errors are found, moving object detection using Gaussian Mixture Models (GMMs) [12] must be highlighted. In this field, most strategies proposed during the last years take as a starting point the work by Stauffer and Grimson [13], which is a seminal work with more than one thousand citations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

Gallego

Cuevas

Mohedano

et al. 2013

IEEE Trans. Signal Process.

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Abstract-Many existing engineering works model the statistical characteristics of the entities under study as normal distributions. These models are eventually used for decision making, requiring in practice the definition of the classification region corresponding to the desired confidence level. Surprisingly enough, however, a great amount of computer vision works using multidimensional normal models leave unspecified or fail to establish correct confidence regions due to misconceptions on the features of Gaussian functions or to wrong analogies with the unidimensional case. The resulting regions incur in deviations that can be unacceptable in high-dimensional models.Here we provide a comprehensive derivation of the optimal confidence regions for multivariate normal distributions of arbitrary dimensionality. To this end, firstly we derive the condition for region optimality of general continuous multidimensional distributions, and then we apply it to the widespread case of the normal probability density function. The obtained results are used to analyze the confidence error incurred by previous works related to vision research, showing that deviations caused by wrong regions may turn into unacceptable as dimensionality increases. To support the theoretical analysis, a quantitative example in the context of moving object detection by means of background modeling is given.

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Section: Results 2 For the Normal Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

Gallego

Cuevas

Mohedano

et al. 2013

IEEE Trans. Signal Process.

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show abstract

“…However, the algorithm remains complex and challenging. There are two notable drawbacks of MoG [3]. First, algorithm parameters require careful tuning.…”

Section: B Background Modeling Using Online Mixture Of Gaussiansmentioning

confidence: 99%

“…The main advantage of including a BS stage in a video surveillance system is the increase in performance of subsequent higher-level video analysis [3].…”

Section: A Background Modeling In the Compressed Domainmentioning

confidence: 99%

Hardware Acceleration of Background Modeling in the Compressed Domain

Popa

Crookes

Miller

2013

IEEE Trans.Inform.Forensic Secur.

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In intelligent video surveillance systems, scalability (of the number of simultaneous video streams) is important. Two key factors which hinder scalability are the time spent in decompressing the input video streams, and the limited computational power of the processor. This paper demonstrates how a combination of algorithmic and hardware techniques can overcome these limitations, and significantly increase the number of simultaneous streams. The techniques used are processing in the compressed domain, and exploitation of the multicore and vector processing capability of modern processors. The paper presents a system which performs background modeling, using a Mixture of Gaussians approach. This is an important first step in the segmentation of moving targets. The paper explores the effects of reducing the number of coefficients in the compressed domain, in terms of throughput speed and quality of the background modeling. The speedups achieved by exploiting compressed domain processing, multicore and vector processing are explored individually. Experiments show that a combination of all these techniques can give a speedup of 170 times on a single CPU compared to a purely serial, spatial domain implementation, with a slight gain in quality.

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“…Uma resenha das técnicas de subtração de fundo pode ser encontrada em McIvor Dentre os diversos métodos na literatura que tratam sobre a Subtração de Fundo, podemos dividi-los basicamente em duas abordagens: a estatística e a não estatística (Hassanpour et al 2011). Todas as duas formas buscam um maior custo-benefício entre qualidade da segmentação e o tempo de processamento.…”

Section: Subtração De Fundo Em Vídeosunclassified

Um Método Para Detecção Em Tempo Real De Objetos Em Vídeos De Alta Definição

MOREIRA¹

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Moreira, Gustavo Costa Gomes; Feijó, Bruno (Advisor). A method for real-time Object Detection in HD videos. Rio de Janeiro, 2014. 85p. DSc. Thesis -Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro.The detection and subsequent tracking of objects in video sequences is a challenge in terms of video processing in real time. In this thesis we propose an detection method suitable for processing high-definition video in real-time. In this method we use a segmentation procedure through integral image of the foreground, which allows a very quick disposal of various parts of the image in each frame, thus achieving a high rate of processed frames per second. Further we extend the proposed method to be able to detect multiple objects in parallel.Furthermore, by using a GPU and techniques that can have its performance enhanced through parallelism, as the operator prefix sum, we can achieve an even better performance of the algorithm, both for the detection of the object, as in the training stage of new classes of objects.

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Video Frame’s Background Modeling: Reviewing the Techniques

Cited by 33 publications

References 5 publications

On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

On the Mahalanobis Distance Classification Criterion for Multidimensional Normal Distributions

Hardware Acceleration of Background Modeling in the Compressed Domain

Um Método Para Detecção Em Tempo Real De Objetos Em Vídeos De Alta Definição

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