Fast multi-class distance transforms for video surveillance


Schouten, Theo E. and Broek, Egon L. van den (2008) Fast multi-class distance transforms for video surveillance. In: Nasser Kehtarnavaz & Matthias F. Carlsohn (Eds.), Real-Time Image Processing 2008. Proceedings of SPIE, 6811 . SPIE, the International Society for Optical Engineering. ISBN 9780819469830

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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.
Item Type:Book Section
Copyright:© 2008 SPIE, the International Society for Optical Engineering
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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