Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Pattern Recognition |
| Untertitel | 36th German Conference, GCPR 2014, Proceedings |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 265-276 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 9783319117515 |
| Publikationsstatus | Veröffentlicht - 15 Okt. 2014 |
| Veranstaltung | 36th German Conference on Pattern Recognition, GCPR 2014 - Münster, Deutschland Dauer: 2 Sept. 2014 → 5 Sept. 2014 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 8753 |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
We present a new global optimization approach for multiple people tracking based on a hierarchical tracklet framework. A new type of tracklets is introduced, which we call tree tracklets. They contain bifurcations to naturally deal with ambiguous tracking situations. Difficult decisions are postponed to a later iteration of the hierarchical framework, when more information is available.We cast the optimization problem as a minimum cost arborescence problem in an acyclic directed graph, where a tracking solution can be obtained in linear time. Experiments on six publicly available datasets show that the method performs well when compared to state-of-the art tracking algorithms.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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Pattern Recognition: 36th German Conference, GCPR 2014, Proceedings. Springer Verlag, 2014. S. 265-276 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8753).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Efficient multiple people tracking using minimum cost arborescences
AU - Henschel, Roberto
AU - Leal-Taixé, Laura
AU - Rosenhahn, Bodo
PY - 2014/10/15
Y1 - 2014/10/15
N2 - We present a new global optimization approach for multiple people tracking based on a hierarchical tracklet framework. A new type of tracklets is introduced, which we call tree tracklets. They contain bifurcations to naturally deal with ambiguous tracking situations. Difficult decisions are postponed to a later iteration of the hierarchical framework, when more information is available.We cast the optimization problem as a minimum cost arborescence problem in an acyclic directed graph, where a tracking solution can be obtained in linear time. Experiments on six publicly available datasets show that the method performs well when compared to state-of-the art tracking algorithms.
AB - We present a new global optimization approach for multiple people tracking based on a hierarchical tracklet framework. A new type of tracklets is introduced, which we call tree tracklets. They contain bifurcations to naturally deal with ambiguous tracking situations. Difficult decisions are postponed to a later iteration of the hierarchical framework, when more information is available.We cast the optimization problem as a minimum cost arborescence problem in an acyclic directed graph, where a tracking solution can be obtained in linear time. Experiments on six publicly available datasets show that the method performs well when compared to state-of-the art tracking algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84908665267&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11752-2_21
DO - 10.1007/978-3-319-11752-2_21
M3 - Conference contribution
AN - SCOPUS:84908665267
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 265
EP - 276
BT - Pattern Recognition
PB - Springer Verlag
T2 - 36th German Conference on Pattern Recognition, GCPR 2014
Y2 - 2 September 2014 through 5 September 2014
ER -