Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 271-277 |
| Seitenumfang | 7 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 3 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 3 Juni 2016 |
| Veranstaltung | 23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016 - Prague, Tschechische Republik Dauer: 12 Juli 2016 → 19 Juli 2016 |
Abstract
Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Physik und Astronomie (insg.)
- Instrumentierung
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in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 3, Nr. 3, 03.06.2016, S. 271-277.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - A GAUSSIAN PROCESS BASED MULTI-PERSON INTERACTION MODEL
AU - Klinger, T.
AU - Rottensteiner, F.
AU - Heipke, C.
N1 - Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2016/6/3
Y1 - 2016/6/3
N2 - Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.
AB - Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.
KW - Gaussian Processes
KW - Interactions
KW - Online
KW - Pedestrians
KW - Tracking
KW - Video
UR - http://www.scopus.com/inward/record.url?scp=85017700110&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-III-3-271-2016
DO - 10.5194/isprs-annals-III-3-271-2016
M3 - Conference article
AN - SCOPUS:85017700110
VL - 3
SP - 271
EP - 277
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 3
T2 - 23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016
Y2 - 12 July 2016 through 19 July 2016
ER -