In this paper, an algorithm for extracting moving objects from a moving background scene that contains complex camera motion is proposed. The robust and efficiently background subtraction algorithm that is able to cope with local scene changes, such as audiences and trees, as well as globe scene changes caused by camera motion. The algorithm is based on a proposed background subtraction method and comprises three steps: the feature-based camera motion estimation, panorama reconstruction and background subtraction. The background subtraction in our method, which introduces risk maps to account for the problem that the background image may not be perfectly aligned to the input images, greatly improves the foreground separation even on the object boundary. We have applied this method to standard image sequences. Experimental results show the proposed method can be successfully provided with the background subtraction capability.
[1]
Christopher G. Harris,et al.
A Combined Corner and Edge Detector
,
1988,
Alvey Vision Conference.
[2]
Harpreet S. Sawhney,et al.
Robust Video Mosaicing through Topology Inference and Local to Global Alignment
,
1998,
ECCV.
[3]
Wolfgang Effelsberg,et al.
Robust background estimation for complex video sequences
,
2003,
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[4]
Til Aach,et al.
Bayesian algorithms for adaptive change detection in image sequences using Markov random fields
,
1995,
Signal Process. Image Commun..
[5]
Til Aach,et al.
Statistical model-based change detection in moving video
,
1993,
Signal Process..