Foreground detection using motion histogram threshold algorithm in high-resolution large datasets

Khan, Fakhri Alam; Nawaz, Muhammad; Imran, Muhammad; Rahman, Arif Ur; Qayum, Fawad

doi:10.1007/s00530-020-00676-3

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…The methods are also evaluated quantitively in terms of ER, VI, GCE and, PRI (Table. [2][3][4][5]. The results prove the superiority of the proposed method in comparison to other methods.…”

Section: Global Consistency Errormentioning

confidence: 53%

“…Histogram thresholding-based approaches use the assumption that adjacent pixels whose value lies within a certain range belong to the same class. Because of their intuitive properties, simplicity of implementation, and computational speed image, these techniques are widely used [2][3][4][5][6]. Edge detection-based approaches assume that pixel values at the boundary between two regions change quickly.…”

mentioning

confidence: 99%

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Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine

Rezaei¹,

rezaei²,

Rezaei³

2023

jist

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Foreground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e.g., in video surveillance. In this paper, we introduce an automatic and efficient Foregroundbackground segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, Kmeans clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.

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“…The methods are also evaluated quantitively in terms of ER, VI, GCE and, PRI (Table. [2][3][4][5]. The results prove the superiority of the proposed method in comparison to other methods.…”

Section: Global Consistency Errormentioning

confidence: 53%

mentioning

confidence: 99%

Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine

Rezaei¹,

rezaei²,

Rezaei³

2023

jist

View full text Add to dashboard Cite

show abstract

“…For the effective value of the threshold at run time in the background subtraction algorithm, the primary component of the foreground detection process, motion is used, in the proposed algorithm. For the said purpose, in the contribution by Khan et al "Foreground detection using motion histogram threshold algorithm in high-resolution large datasets," the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley [7]. This proposed algorithm was tested using four recommended video sequences, including indoor and outdoor shoots, and were compared with five high ranked algorithms.…”

Section: Performance Improvementsmentioning

confidence: 99%