Authors: Amina Ourchani; Zine-Eddine Baarir; Abdelmalik Taleb-Ahmed
Addresses: Department of Electrical Engineering, AISEL, University of Mohamed Khider, B.P 145 RP, Biskra, Algeria ' Department of Electrical Engineering, AISEL, University of Mohamed Khider, B.P 145 RP, Biskra, Algeria ' Laboratory of industrial and Human Automation, Mechanics and Computer Sciences, University of Valenciennes, 59313 Valenciennes Cedex 9, France
Abstract: Segmentation of moving objects in video sequence is essential task in computer vision. This paper focuses on developing a new method for discriminate moving objects from a static background, focusing on the combination of motion, colour and texture features. First, we have used block-matching for computing the optical flow, we also have taken in consideration the result of frame difference, to improve the quality of the optical flow. Moreover, we have used the k-means clustering algorithm owing to group the pixels, having similar features. Second, the result of the grouping pixels is used as an input in Chan-Vese model, in order to attract the evolving contour of moving objects contours. To evaluate the performance of our proposed method, we experiment it on challenging sequences. It has shown that our method provides an improved segmentation results.
Keywords: segmentation; moving object; optical flow; colour feature; texture feature; k-means; frame difference; Chan-Vese model; block-matching; occlusion.
International Journal of Intelligent Systems Technologies and Applications, 2018 Vol.17 No.1/2, pp.195 - 209
Received: 25 Feb 2017
Accepted: 27 Aug 2017
Published online: 03 May 2018 *