Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-Based Spatial Segmentation of Video Guided by Depth and Motion Information
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Multiple Object Tracking Based on Adaptive Depth Segmentation
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Stereo-based pedestrian detection for collision-avoidance applications
IEEE Transactions on Intelligent Transportation Systems
Moving object segmentation by background subtraction and temporal analysis
Image and Vision Computing
IEEE Transactions on Multimedia
Spatiotemporal video segmentation based on graphical models
IEEE Transactions on Image Processing
Video segmentation for content-based coding
IEEE Transactions on Circuits and Systems for Video Technology
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In the case of cluttered backgrounds or low quality video input, automatic video object segmentation based on spatial-temporal information is still a problem without a general solution. A new approach is introduced in this work to deal with this problem by using depth information. The proposed approach obtains the initial object masks based on depth density image and motion segmentation. The objects boundaries are obtained by updating object masks using a simultaneous combination of multiple cues, including spatial location, colour, depth and motion, within a maximum likelihood method. The experimental result shows that this method is effective and has good output in cluttered backgrounds.