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Proceedings Paper

Fast multi-class distance transforms for video surveillance
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Paper Abstract

A distance transformation (DT) takes a binary image as input and generates a distance map image in which the value of each pixel is its distance to a given set of object pixels in the binary image. In this research, DT's for multi class data (MCDTs) are developed which generate both a distance map and a class map containing for each pixel the class of the closest object. Results indicate that the MCDT based on the Fast Exact Euclidean Distance (FEED) method is a factor 2 tot 4 faster than MCDTs based on exact or semi-exact euclidean distance (ED) transformations, and is only a factor 2 to 4 slower than the MCDT based on the crude city-block approximation of the ED. In the second part of this research, the MCDTs were adapted such that they could be used for the fast generation of distance and class maps for video sequences. The frames of the sequences contain a number of fixed objects and a moving object, where each object has a separate label. Results show that the FEED based version is a factor 2 to 3.5 faster than the fastest of all the other video-MCDTs which is based on the chamfer 3,4 distance measure. FEED is even a factor 3.5 to 10 faster than another fast exact ED transformation. With video, multi class FEED it will be possible to measure distances from a moving object to various identified stationary objects with nearly the frame rate of a webcam. This will be very useful when the risk exists that objects move outside surveillance limits.

Paper Details

Date Published: 26 February 2008
PDF: 11 pages
Proc. SPIE 6811, Real-Time Image Processing 2008, 681107 (26 February 2008); doi: 10.1117/12.766408
Show Author Affiliations
Theo E. Schouten, Radboud Univ. Nijmegen (Netherlands)
Egon L. van den Broek, Univ. of Twente (Netherlands)

Published in SPIE Proceedings Vol. 6811:
Real-Time Image Processing 2008
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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