Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 |
| Seiten | 120-127 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2011 |
| Veranstaltung | 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 - Barcelona, Spanien Dauer: 6 Nov. 2011 → 13 Nov. 2011 |
Publikationsreihe
| Name | Proceedings of the IEEE International Conference on Computer Vision |
|---|
Abstract
Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Most tracking methods make the assumption that each pedestrian's motion is independent, thereby ignoring the complex and important interaction between subjects. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, considering social and grouping behavior, and second, using a global optimization scheme to solve the data association problem. Results on three challenging publicly available datasets show our method outperforms state-of-the-art tracking systems.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011. 2011. S. 120-127 6130233 (Proceedings of the IEEE International Conference on Computer Vision).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Everybody needs somebody
T2 - 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
AU - Leal-Taixé, Laura
AU - Pons-Moll, Gerard
AU - Rosenhahn, Bodo
PY - 2011
Y1 - 2011
N2 - Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Most tracking methods make the assumption that each pedestrian's motion is independent, thereby ignoring the complex and important interaction between subjects. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, considering social and grouping behavior, and second, using a global optimization scheme to solve the data association problem. Results on three challenging publicly available datasets show our method outperforms state-of-the-art tracking systems.
AB - Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Most tracking methods make the assumption that each pedestrian's motion is independent, thereby ignoring the complex and important interaction between subjects. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, considering social and grouping behavior, and second, using a global optimization scheme to solve the data association problem. Results on three challenging publicly available datasets show our method outperforms state-of-the-art tracking systems.
UR - http://www.scopus.com/inward/record.url?scp=84856640075&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2011.6130233
DO - 10.1109/ICCVW.2011.6130233
M3 - Conference contribution
AN - SCOPUS:84856640075
SN - 9781467300629
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 120
EP - 127
BT - 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011
Y2 - 6 November 2011 through 13 November 2011
ER -