Preprint Article Version 1 This version is not peer-reviewed

Improving Video Segmentation by Fusing Depth Cues and the ViBe Algorithm

Version 1 : Received: 20 March 2017 / Approved: 20 March 2017 / Online: 20 March 2017 (10:21:40 CET)

A peer-reviewed article of this Preprint also exists.

Zhou, X.; Liu, X.; Jiang, A.; Yan, B.; Yang, C. Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm. Sensors 2017, 17, 1177. Zhou, X.; Liu, X.; Jiang, A.; Yan, B.; Yang, C. Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm. Sensors 2017, 17, 1177.

Journal reference: Sensors 2017, 17, 1177
DOI: 10.3390/s17051177

Abstract

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

Subject Areas

object detection; background subtraction; video surveillance; Kinect sensor fusion

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