By Topic

Multiple object tracking by hierarchical association of spatio-temporal data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Beleznai, Csaba ; AIT - Austrian Inst. of Technol., Vienna, Austria ; Schreiber, D.

This paper presents a data-oriented tracking framework which aims to recover the spatio-temporal trajectories for an unknown number of interacting objects appearing and disappearing at arbitrary times. Data association is performed at three-levels of a hierarchy: (i) first, trajectory segments and an associated quality measure are generated by a local analysis of the space-time distribution of observations; (ii) a conservatively constrained association step links nearby consistent segments into intermediate trajectory fragments; and (iii) a last association step taking into account all available data (observations, trajectory fragments) generates the final trajectory estimates. The association step relies on the Hungarian algorithm and it also considers detection responses below the detection threshold as evidence associated with high ambiguity. We demonstrate the feasibility of the proposed approach applied to the pedestrian tracking task on two challenging datasets.

Published in:

Image Processing (ICIP), 2010 17th IEEE International Conference on

Date of Conference:

26-29 Sept. 2010