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International Journal of Computer Vision

Erschienen in:

01.10.2014

Detection and Tracking of Occluded People

verfasst von: Siyu Tang, Mykhaylo Andriluka, Bernt Schiele

Erschienen in: International Journal of Computer Vision | Ausgabe 1/2014

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Abstract

We consider the problem of detection and tracking of multiple people in crowded street scenes. State-of-the-art methods perform well in scenes with relatively few people, but are severely challenged by scenes with many subjects that partially occlude each other. This limitation is due to the fact that current people detectors fail when persons are strongly occluded. We observe that typical occlusions are due to overlaps between people and propose a people detector tailored to various occlusion levels. Instead of treating partial occlusions as distractions, we leverage the fact that person/person occlusions result in very characteristic appearance patterns that can help to improve detection results. We demonstrate the performance of our occlusion-aware person detector on a new dataset of people with controlled but severe levels of occlusion and on two challenging publicly available benchmarks outperforming single person detectors in each case.

Vorheriger Artikel Using Bounded Diameter Minimum Spanning Trees to Build Dense Active Appearance Models

Nächster Artikel Automatic and Efficient Human Pose Estimation for Sign Language Videos

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The training and test datasets are available at www.d2.mpi-inf.mpg.de/datasets

http://www.gris.tu-darmstadt.de/~aandriye

http://people.csail.mit.edu/hpirsiav

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Titel: Detection and Tracking of Occluded People
verfasst von: Siyu Tang
Mykhaylo Andriluka
Bernt Schiele
Publikationsdatum: 01.10.2014
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1/2014
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-013-0664-6

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