Video-object segmentation and 3D-trajectory estimation for monocular video sequences

Feng Xu, Kin Man Lam, Qionghai Dai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

In this paper, we describe a video-object segmentation and 3D-trajectory estimation method for the analysis of dynamic scenes from a monocular uncalibrated view. Based on the color and motion information among video frames, our proposed method segments the scene, calibrates the camera, and calculates the 3D trajectories of moving objects. It can be employed for video-object segmentation, 2D-to-3D video conversion, video-object retrieval, etc. In our method, reliable 2D feature motions are established by comparing SIFT descriptors among successive frames, and image over-segmentation is achieved using a graph-based method. Then, the 2D motions and the segmentation result iteratively refine each other in a hierarchically structured framework to achieve video-object segmentation. Finally, the 3D trajectories of the segmented moving objects are estimated based on a local constant-velocity constraint, and are refined by a Hidden Markov Model (HMM)-based algorithm. Experiments show that the proposed framework can achieve a good performance in terms of both object segmentation and 3D-trajectory estimation.
Original languageEnglish
Pages (from-to)190-205
Number of pages16
JournalImage and Vision Computing
Volume29
Issue number2-3
DOIs
Publication statusPublished - 1 Jan 2011

Keywords

  • 2D-to-3D video conversion
  • 3D trajectory estimation
  • Video-object segmentation

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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