Statistical background modeling for non-stationary camera
Pattern Recognition Letters
Motion detection with nonstationary background
Machine Vision and Applications
Separation of Professional and Amateur Video in Large Video Collections
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
An object-based visual attention model for robotic applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Object segmentation in videos from moving camera with MRFs on color and motion features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we propose a new method for detection and tracking of moving objects from a moving camera image sequence using robust statistics and active contour models. we assume that the apparent background motion between two consecutive image frames can be approximated by affine transformation. In order to register the static background, we estimate affine transformation parameters using LMedS (Least Median of Squares) method which is a kind of robust statistics. Split-and-merge contour models are employed for tracking multiple moving objects which have been recently proposed by the authors. Image energy of contour models is defined based on the image which is obtained by subtracting the previous frame transformed with estimated affine parameters from the current frame. We have implemented the method on an image processing system which consists of DSP boards for real-time tracking of moving objects from a moving camera image sequence.