Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points

Andrea Cavagna, Chiara Creato, Lorenzo Del Castello, Stefania Melillo, Leonardo Parisi, Massimiliano Viale

2016

Abstract

The interest in 3D dynamical tracking is growing in fields such as robotics, biology and fluid dynamics. Recently, a major source of progress in 3D tracking has been the study of collective behaviour in biological systems, where the trajectories of individual animals moving within large and dense groups need to be reconstructed to understand the behavioural interaction rules. Experimental data in this field are generally noisy and at low spatial resolution, so that individuals appear as small featureless objects and trajectories must be retrieved by making use of epipolar information only. Moreover, optical occlusions often occur: in a multicamera system one or more objects become indistinguishable in one view, potentially subjected to loss of identity over long-time trajectories. The most advanced 3D tracking algorithms overcome optical occlusions making use of set-cover techniques, which however have to solve NP-hard optimization problems. Moreover, current methods are not able to cope with occlusions arising from actual physical proximity of objects in 3D space. Here, we present a new method designed to work directly on (3D + 1) clouds of points representing the full spatio-temporal evolution of the moving targets. We can then use a simple connected components labeling routine, which is linear in time, to solve optical occlusions, hence lowering from NP to P the complexity of the problem. Finally, we use normalized cut spectral clustering to tackle 3D physical proximity.

Paper Citation

in Harvard Style

Cavagna A., Creato C., Del Castello L., Melillo S., Parisi L. and Viale M. (2016). Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 679-685. DOI: 10.5220/0005770106790685

in Bibtex Style

@conference{visapp16,
author={Andrea Cavagna and Chiara Creato and Lorenzo Del Castello and Stefania Melillo and Leonardo Parisi and Massimiliano Viale},
title={Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={679-685},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005770106790685},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Towards a Tracking Algorithm based on the Clustering of Spatio-temporal Clouds of Points
SN - 978-989-758-175-5
AU - Cavagna A.
AU - Creato C.
AU - Del Castello L.
AU - Melillo S.
AU - Parisi L.
AU - Viale M.
PY - 2016
SP - 679
EP - 685
DO - 10.5220/0005770106790685