In the application of video surveillance target detection

the shadow will directly affect the accuracy of the target detection in the scenes

so the shadow suppression algorithm is particularly important.The traditional algorithm which is based on hue

saturation

and value(HSV) to detect shadow is popular.Inspired by color perception mechanism of human visual system

this algorithm detects the shadow by the luminance ratio between the current video frame and background model.We propose a shadow elimination algorithm based on HSV spatial feature and texture features to overcome the shortcoming of the luminance ratio between them

which causes the moving target to be mistaken for the shadow. The Gaussian mixture model can effectively overcome the interference caused by the change of illumination and periodic disturbance of background image.First

the mixed Gaussian model(essentially 3 to 5) is used to characterize each pixel in the input images

and the updating mixed Gaussian model is obtained after the new input frame image.Each pixel of the current image is matched with the mixed Gaussian model

and if it is successful

the point belongs to background;otherwise

it belongs to foreground.The algorithm based on HSV color space can detect the shadow accurately by calculating the luminance ratio between the current video frame and the background model because the hue and saturation values are approximate in the shadow compared with the ones in the background

and the luminance value of shadow pixels is lower than the luminance value of the background pixels.The luminance ratio between them is usually 0.7 to 1.Therefore

the moving target can be obtained by combining the foreground detected by Gaussian mixture background model with the shadow detected by the method based on HSV color space.The traditional algorithms based on HSV color space can obtain the accurate detection results

but the moving target is often mistaken for shadows seriously in the video frames.To overcome this problem

we use texture featuresthat conclude local binary pattern(LBP) and OTSU to extract the moving target.LBP is an operator of gray-scale variation.A smaller threshold is selected

which is compared with the difference value between gray value of the central pixel and gray value of its corresponding neighborhood pixel.If the difference is greater than the threshold

it is marked as 1;otherwise

it is marked as 0.Therefore

we can obtain a description of the texture change at the location of the central pixel.The LBP operator extracts local texture features by the original gray level of the image.OSTU is a maximum interclass variance method.If the shadow and the target have large variances

then the two parts have much difference.When the partial target is regarded as shadows or part of the shadow is regarded as the target

the difference of two parts becomes smaller.Thus

the largest variance segmentation between classes can result in a minimum probability of misclassification.According to the gray feature of image

the image is divided into the shadow and the target by OTSU.The complete moving target is obtained by OR operator of combining the foreground

which is respectivelyextracted by OTSU and LBP operator with the result that is extracted by HSV. The proposed algorithm is applied to several different shadow videos

which are included in CVPR-ATON standard video library and CAVIAR standard video library.Experimental results show that when the threshold of luminance ratio

which is applied to detect the shadow in HSV color space

remains unchanged

the moving target is extracted accurately and its shadows are basically eliminated.Compared with other traditional algorithms

such as statistical parametric(SP) approach

statistical nonparametric(SNP) approach

and two kinds of deterministic non-model(DNM1

DNM2) approach

the proposed algorithm obtain the better result.Experimental results show that the proposed algorithm has much better increment of about 10% than the forehead algorithms in terms of the average of shadow detection rate and shadow discrimination rate.Although in the Intelligent Room video

the shadow discrimination rate is 1.6% lower than that of the DNM2 algorithm and the shadow discrimination rate is 2.9% lower than that of the SP algorithm in the video of Laboratory;thus

the algorithm improves the shadow detection rate by 29% and 27.2%

respectively.In the real-time test

this algorithm can process 12~15 frames per second

which can satisfy the real-time needs. Although the traditional algorithms that use HSV has great effect in the shadow elimination

the moving target may easily be interpreted as the shadow.The texture featuresthat include LBP and OTSU can make up for this shortcoming

wepropose the video shadow elimination algorithm by combining HSV with Texture features.Compared with other algorithms

our method can obtain more accurate shadow detection result and has much better advantages in terms of average shadow detection rate and shadow discrimination.Our method can be applied to intelligent video surveillance

remote sensing images

and human-computer interaction.Our future work will focus on improving the real-time performance.