Detecting moving objects from a video taken by a moving camera using sequential inference of background images

This paper proposes a method of detecting moving objects using sequential inference of the background in a video taken with a moving camera. In the video taken using a moving camera, all positions of pixels change every frame. The positions of the background pixels in the image frame T are not the same as the positions of the background pixels in the image frame T + 1. 2D projective transform can be used to find changes in the pixel position every frame. Bilinear interpolation with four nearest pixels around the pixel in image frame T which corresponds to a pixel in the image frame T+1 can be used for creating a background model at T + 1. Having obtained the background model, a pixel in image frame T + 1 can be determined if it is a background pixel or a foreground pixel. The detection results of the proposed method are compared with the ground truth to determine the effectiveness of the proposed method.

[1]  Gaurav S. Sukhatme,et al.  Detecting Moving Objects using a Single Camera on a Mobile Robot in an Outdoor Environment , 2004 .

[2]  Thierry Bouwmans,et al.  Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey , 2011 .

[3]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[4]  K. A. Joshi,et al.  A Survey on Moving Object Detection and Tracking in Video Surveillance System , 2012 .

[5]  N. Satish Kumar,et al.  Adaptive Background Modeling and Foreground Detection in Video Sequence Using Artificial Neural Network , 2012 .

[6]  Thierry Bouwmans,et al.  Foreground detection based on low-rank and block-sparse matrix decomposition , 2012, 2012 19th IEEE International Conference on Image Processing.

[7]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[8]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[9]  Rozenn Dahyot Unsupervised Camera Motion Estimation and Moving Object Detection in Videos , 2006 .

[10]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[11]  Jin Young Choi,et al.  Intelligent visual surveillance — A survey , 2010 .