Tracking by Hierarchical Representation of Target Structure

Authors:
Nicole M. Artner;Salvador B. Mármol;Csaba Beleznai;Walter G. Kropatsch
Affiliations:
Smart Systems Division, Austrian Research Centers GmbH - ARC, Vienna, Austria;PRIP, Vienna University of Technology, Vienna, Austria;Smart Systems Division, Austrian Research Centers GmbH - ARC, Vienna, Austria;PRIP, Vienna University of Technology, Vienna, Austria
Venue:
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2008

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Abstract

Tracking of spatially extended targets with variable shape, pose and appearance is a highly challenging task. In this work we propose a novel tracking approach using an incrementally generated part-based description to obtain a specific representation of target structure. The hierarchical part-based representation is learned in a generative manner from a large set of simple local features. The spatial and temporal density of observed part combinations is estimated by performing statistics over temporally aggregated data. Detected stable combinations consisting of multiple simpler parts encompass local, specific structures, which can efficiently guide a spatio-temporal association step of coherently moving image regions, which are part of the same target. The concept of our approach is proved and evaluated in several experiments.