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Proceedings Paper

A Viterbi tracker for local features
Author(s): Gary Baugh; Anil Kokaram
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Paper Abstract

The long term tracking of sparse local features in an image is important for many applications including camera calibration for stereo applications, camera or global motion estimation and people surveillance. The majority of existing tracking frameworks are based on some kind of prediction/correction idea e.g. KLT and Particle Filters. However, given a careful selection of interest points throughout the sequence, the problem of tracking can be solved with the Viterbi algorithm. This work introduces a novel approach to interest point selection for tracking using the Mean Shift algorithm over short time windows. The resulting points are then articulated within a Viterbi algorithm for creating very long term tracking data. The tracks are shown to be more accurate than traditional KLT implementations and also do not suffer from accumulation of error with time.

Paper Details

Date Published: 18 January 2010
PDF: 9 pages
Proc. SPIE 7543, Visual Information Processing and Communication, 75430L (18 January 2010); doi: 10.1117/12.839469
Show Author Affiliations
Gary Baugh, Trinity College Dublin (Ireland)
Anil Kokaram, Trinity College Dublin (Ireland)

Published in SPIE Proceedings Vol. 7543:
Visual Information Processing and Communication
Amir Said; Onur G. Guleryuz, Editor(s)

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