GDL

Journal from gdlhub / 2018-06-02 09:59:14
Oleh : M. Arief Soeleman, Moch. Hariadi, Eko Mulyanto Y, dMauridhi H. Purnomo, Kursor
Dibuat : 2018-06-02, dengan 1 file

Keyword : K-Means, distance measure, moving object segmentation
Url : http://kursorjournal.org/index.php/kursor/article/view/75
Sumber pengambilan dokumen : WEB

Segmentation of moving objects is one of the challenging research areas for video

surveillance application. The success of object changing position for segmentation is when

the moving object completely separate the foreground from its background of frame. It

depends on many factors, including the use of suitable clustering method to differentiate

the pixels of the foreground and background. This paper propose to use k-means as

clustering method for moving object segmentation. The method is evaluated on several

distance measures. Several steps are performed to conduct the moving object

segmentation, such as frame subtraction, median filtering, and noise removal. These steps

are proposed to improve the achievement of moving object segmentation. The performance

are evaluated by using Mean of Square Error and Peak Signal to Noise Error. The value of

both measurement are 135.02 and 25.52. The experimental result shows that the moving

object segmentation performs the best result on Minkowski distance.

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