Robust Multi-Human Tracking by Detection Update using Reliable Temporal Information

Lu Wang, Qingxu Deng, Mingxing Jia

2014

Abstract

In this paper, we present a multiple human tracking approach that takes the single frame human detection results as input, and associates them hierarchically to form trajectories while improving the original detection results by making use of reliable temporal information. It works by first forming tracklets, from which reliable temporal information can be extracted, and then refining the detection responses inside the tracklets. After that, local conservative tracklets association is performed and reliable temporal information is propagated across tracklets. The global tracklet association is done lastly to resolve association ambiguities. Comparison with two state-of-the-art approaches demonstrates the effectiveness of the proposed approach.

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Paper Citation


in Harvard Style

Wang L., Deng Q. and Jia M. (2014). Robust Multi-Human Tracking by Detection Update using Reliable Temporal Information.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 387-396. DOI: 10.5220/0004733203870396


in Bibtex Style

@conference{visapp14,
author={Lu Wang and Qingxu Deng and Mingxing Jia},
title={Robust Multi-Human Tracking by Detection Update using Reliable Temporal Information},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={387-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004733203870396},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Robust Multi-Human Tracking by Detection Update using Reliable Temporal Information
SN - 978-989-758-009-3
AU - Wang L.
AU - Deng Q.
AU - Jia M.
PY - 2014
SP - 387
EP - 396
DO - 10.5220/0004733203870396