Adaptive learning of multi-subspace for foreground detection under illumination changes

Dong, Yuanqiang; DeSouza, Guilherme N.

doi:10.1016/j.cviu.2010.08.003

Cited by 33 publications

(18 citation statements)

References 15 publications

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“…Durante la etapa de acondicionamiento de la imagen, se deben analizar los efectos generadores de degradación a fin de implementar medidas para reducir su impacto, entre los fenómenos causantes de la degradación es posible encontrar [1, 13,[17][18][24][25][26][27][28]:…”

Section: A Imágenes Digitalesunclassified

“…En las aplicaciones relacionadas con el seguimiento de personas (u objetos), la detección del movimiento se fundamenta en la substracción de la información del fondo y la captura de las variables temporales [1,[4][5][6][7][9][10][11][12][13]20,22,26].…”

Section: B Iluminaciónunclassified

“…La situación descrita hace necesaria la utilización de técnicas de restauración a fin obtener información de utilidad a partir de la imagen degradada [8,9,12,13], durante el presente artículo se revisan los efectos que provocan la degradación de la información a causa de las fuentes de iluminación existentes durante la adquisición de la imagen y las posibles medidas para solucionar la problemática.…”

Section: Introductionunclassified

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Obtención automática de siluetas a partir de imágenes adquiridas en condiciones de iluminación no controlada

S¹,

Archila²

2012

Iteckne

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Section: A Imágenes Digitalesunclassified

Section: B Iluminaciónunclassified

Section: Introductionunclassified

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Obtención automática de siluetas a partir de imágenes adquiridas en condiciones de iluminación no controlada

S¹,

Archila²

2012

Iteckne

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“…Moreover, we select models adaptive to fast lighting change (e.g. ALPCA [7], TGMM [2], HGMM [3] and MGMM [4]) with the optimal parameters selected to do comparative experiments. The experiments were run on a 2.2GB Intel 2-core machine with MatlabV7.1.…”

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confidence: 99%

Background subtraction using semantic-based hierarchical GMM

Zhao

Liu

et al. 2012

Electron. Lett.

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Background including a long-period fast illumination variation is commonly assumed to be foreground by mistake. To solve this problem, proposed is a semantic-based hierarchical Gaussian mixture model integrated with an illumination detection approach. First, autocorrelation-based features for broad identification of background lighting changes and foreground in short-term sequences are presented. Then, the hierarchical Gaussians representing different background illumination variations are maintained. The effectiveness of the proposed method is demonstrated using experiments on pedestrian detection in fast lighting change.Introduction: Background subtraction is a commonly used approach to foreground detection. However, fast illumination variation of background presents a challenge for accurate detection results of foreground. Ordinarily, fast lighting change is considered to appear in regions rather than at pixels. Therefore, many approaches focus on the integration of regional information into a pixel-wise background model. The Gaussian mixture model (GMM) [1] is the most representative background model incorporated with other methods [2-4], so we are mainly concerned with the GMM in this Letter. Tian et al. brought a regional texture measure into the GMM (TGMM) and claimed the solution to accurate foreground detection in fast illumination variation [2]. A hierarchical GMM (HGMM) of different scales was devised to discriminate fast illumination change from foreground [3]. Meanwhile, a memorising GMM (MGMM) was proposed to tackle fast lighting change at pixels in a scene [4]. In the MGMM, Gaussians representing background were labelled and recorded in long-term memory. Actually, fast lighting change results from temporal intensity variations at pixels rather than those in regions. Thus, it is advisable to memorise the former background states deriving from fast lighting change. However, it is unadvisable to label the similar background states excessively without any intended discrimination between different illumination variations.In this Letter, we propose a semantic-based hierarchical GMM (S-HGMM) for long-term memory of periodical fast lighting change. A criterion for illumination detection is first made to identify different lighting changes in short-term sequences. Then, we design a hierarchical GMM including a sequence of short-term Gaussians and a single long-term one tagged semantically with a label of fast lighting change according to the illumination detection results.Background: The GMM is a pixel-wise mixture of K Gaussian distributions representing the recent states. The probability of intensity X t is P(X t ) = K k=1 v k,t × h(X t , m k,t , S k,t ), where h obeys the Gaussian distribution. m k,t , S k,t and v k,t represent the current mean, the covariance matrix and the weight estimation of Gaussian k, respectively. The covariance matrix S k,t is referred to as S k,t = s k,t × I, assuming the red, green and blue pixel values to be independent. A new pixel value X t + 1 is checked against each Gaussi...

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“…First, since we are only interested in feature points on the actual object, the background must be subtracted from the foreground using the algorithm presented in [4]. After that, the framework finds matching points between all pairs of stereo images using the SIFT algorithm [3], [11] implemented as a Matlab toolbox [6].…”

Section: ) Determining Correspondences In the Image Domainmentioning

confidence: 99%

3D human modeling using virtual multi-view stereopsis and object-camera motion estimation

Lam

Hong

DeSouza

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a method for multi-view 3D modeling of human bodies using virtual stereopsis. The algorithm expands and improves the method used in [5], but unlike that method, our approach does not require multiple calibrated cameras and/or carefully-positioned turn tables. Instead, an algorithm using SIFT feature extraction is employed and an accurate motion estimation is performed to calculate the position of virtual cameras around the object. That is, by employing a single pair of cameras mounted on a same tripod, our algorithm computes the relative pose between camera and object and creates virtual cameras from the consecutive images in the video sequence. Besides not requiring any special setup, another advantage of our method is in the simplicity to obtain denser models if necessary: by only increasing the number of sampled images during the object-camera motion. As the quantitative results presented here demonstrate, our method compares to the PMVS method, while it makes it much simpler and cost-effective to implement.

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Adaptive learning of multi-subspace for foreground detection under illumination changes

Cited by 33 publications

References 15 publications

Obtención automática de siluetas a partir de imágenes adquiridas en condiciones de iluminación no controlada

Obtención automática de siluetas a partir de imágenes adquiridas en condiciones de iluminación no controlada

Background subtraction using semantic-based hierarchical GMM

3D human modeling using virtual multi-view stereopsis and object-camera motion estimation

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