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Proceedings Paper

A recurrent velocity filter for detecting large numbers of moving objects
Author(s): R. Porter; A. Fraser; R. Loveland; E. Rosten
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Paper Abstract

We present a method for detecting a large number of moving targets, such as cars and people, in geographically referenced video. The problem is difficult, due to the large and variable number of targets which enter and leave the field of view, and due to imperfect geo-projection and registration. In our method, we assume feature extraction produces a collection of candidate locations (points in 2D space) for each frame. Some of these locations are real objects, but many are false alarms. Typical feature extraction might be frame differencing, or target recognition. For each candidate location, and at each time step, our algorithm outputs a velocity estimate and confidence which can be thresholded to detect objects with constant velocity. In this paper we derive the algorithm, investigate the free parameters, and compare its performance to a multi-target tracking algorithm.

Paper Details

Date Published: 16 April 2008
PDF: 9 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69690C (16 April 2008); doi: 10.1117/12.778505
Show Author Affiliations
R. Porter, Los Alamos National Lab. (United States)
A. Fraser, Los Alamos National Lab. (United States)
R. Loveland, Los Alamos National Lab. (United States)
E. Rosten, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 6969:
Signal and Data Processing of Small Targets 2008
Oliver E. Drummond, Editor(s)

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