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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Stephen A. Burns
  • Vol. 26, Iss. 2 — Feb. 1, 2009
  • pp: 342–349

Moving object detection based on shape prediction

Xiang Zhang and Jie Yang  »View Author Affiliations


JOSA A, Vol. 26, Issue 2, pp. 342-349 (2009)
http://dx.doi.org/10.1364/JOSAA.26.000342


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Abstract

Statistical analysis is widely used for moving object detection from video sequences, and generally it assumes that the recent segmentations are the desirable samples. However, we think the recent segmentations are not desirable for the detection of deformable objects, such as a pedestrian. We present an object detection framework that is designed to select the most desirable samples from all historical segmentations for statistical analysis. Central to this algorithm is that the shape evolution of the deformable object is learned from historical segmentations by an autoregressive model. Based on the learned model, the shape of the moving object in a current frame is predicted. Those historical segmentations that show similar shape to the predicted shape are selected for statistical analysis instead of the recent segmentations. Definitive experiments demonstrate the performance of the proposed method.

© 2009 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(200.3050) Optics in computing : Information processing
(330.1880) Vision, color, and visual optics : Detection
(150.1135) Machine vision : Algorithms

ToC Category:
Image Processing

History
Original Manuscript: July 15, 2008
Revised Manuscript: October 25, 2008
Manuscript Accepted: November 22, 2008
Published: January 28, 2009

Citation
Xiang Zhang and Jie Yang, "Moving object detection based on shape prediction," J. Opt. Soc. Am. A 26, 342-349 (2009)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-26-2-342

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