Integrating pedestrian simulation, tracking and event detection for crowd analysis

被引：0

作者：

Butenuth, Matthias ^{[1
]}

Burkert, Florian ^{[1
,3
]}

Kneidl, Angelika ^{[2
]}

Borrmann, Andre ^{[2
]}

Schmidt, Florian

Hinz, Stefan ^{[3
]}

Sirmacek, Beril ^{[4
]}

Hartmann, Dirk ^{[5
]}

机构：

[1] Tech Univ Munich, D-8000 Munich, Germany

[2] Tech Univ Munich, Comp Modeling & Simulat Grp, Munich, Germany

[3] Karlsruhe Inst Technol, Photogrammetry & Remote Sensing, Karlsruhe, Germany

[4] German Aerosp Ctr, Inst Remote Sensing, Cologne, Germany

[5] Siemens AG, Corp Technol, Munich, Germany

来源：

2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCV WORKSHOPS) | 2011年

关键词：

HIDDEN MARKOV-MODELS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, an overall framework for crowd analysis is presented. Detection and tracking of pedestrians as well as detection of dense crowds is performed on image sequences to improve simulation models of pedestrian flows. Additionally, graph-based event detection is performed by using Hidden Markov Models on pedestrian trajectories utilizing knowledge from simulations. Experimental results show the benefit of our integrated framework using simulation and real-world data for crowd analysis.

引用

页数：8

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