A fast approach of object segmentation for video sequence

Authors:
Yi-Ching Liaw;Bo-Shuan Chiu;Jim Z. C. Lai;Tsung-Jen Huang
Affiliations:
Nanhua University, Chiayi, Taiwan, R.O.C.;Nanhua University, Chiayi, Taiwan, R.O.C.;National Taiwan Ocean University, Keelung, Taiwan, R.O.C.;Industrial Technology Research Institute, Hsinchu, Taiwan, R.O.C.
Venue:
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Year:
2007

Citing 7
Cited 0

Model-based object tracking in monocular image sequences of road traffic scenes

International Journal of Computer Vision
Computer and Robot Vision

Computer and Robot Vision
Background Subtraction and Shadow Detection in Grayscale Video Sequences

SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
Detection of moving cast shadows for object segmentation

IEEE Transactions on Multimedia
The MPEG-4 video standard verification model

IEEE Transactions on Circuits and Systems for Video Technology
Efficient moving object segmentation algorithm using background registration technique

IEEE Transactions on Circuits and Systems for Video Technology
Insignificant shadow detection for video segmentation

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a novel video object segmentation approach. The proposed approach extracts objects from a frame in a video stream using the difference information between the mean-removed versions of the current and referenced frames. Due to the mean-removed version of a frame reduces the influence of light variation on the frame and reserves the texture information of the frame, the proposed approach can effectively segment objects for video sequences and remove shadow pixels. Experimental results show that the proposed approach has the least computation time among object segmentation approaches with shadow removal capability. Compared with the available approaches, our approach reduces the computation time by 7% to 58% with better segmentation accuracy.