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Proceedings Paper

Intelligent pre-processing for fast moving object detection
Author(s): Chris Poppe; Sarah De Bruyne; Gaëtan Martens; Peter Lambert; Rik Van de Walle
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Paper Abstract

Detection and segmentation of objects of interest in image sequences is the first major processing step in visual surveillance applications. The outcome is used for further processing, such as object tracking, interpretation, and classification of objects and their trajectories. To speed up the algorithms for moving object detection, many applications use techniques such as frame rate reduction. However, temporal consistency is an important feature in the analysis of surveillance video, especially for tracking objects. Another technique is the downscaling of the images before analysis, after which the images are up-sampled to regain the original size. This method, however, increases the effect of false detections. We propose a different pre-processing step in which we use a checkerboard-like mask to decide which pixels to process. For each frame the mask is inverted to avoid that certain pixel positions are never analyzed. In a post-processing step we use spatial interpolation to predict the detection results for the pixels which were not analyzed. To evaluate our system we have combined it with a background subtraction technique based on a mixture of Gaussian models. Results show that the models do not get corrupted by using our mask and we can reduce the processing time with over 45% while achieving similar detection results as the conventional technique.

Paper Details

Date Published: 25 March 2008
PDF: 8 pages
Proc. SPIE 6978, Visual Information Processing XVII, 69780S (25 March 2008); doi: 10.1117/12.777034
Show Author Affiliations
Chris Poppe, Ghent Univ. (Belgium)
Sarah De Bruyne, Ghent Univ. (Belgium)
Gaëtan Martens, Ghent Univ. (Belgium)
Peter Lambert, Ghent Univ. (Belgium)
Rik Van de Walle, Ghent Univ. (Belgium)

Published in SPIE Proceedings Vol. 6978:
Visual Information Processing XVII
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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