Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings |
| Untertitel | 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 166-174 |
| Seitenumfang | 9 |
| ISBN (elektronisch) | 9781467383905 |
| Publikationsstatus | Veröffentlicht - 11 Feb. 2015 |
| Veranstaltung | 15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015 - Santiago, Chile Dauer: 11 Dez. 2015 → 18 Dez. 2015 |
Publikationsreihe
| Name | Proceedings of the IEEE International Conference on Computer Vision |
|---|---|
| Band | 2015-February |
| ISSN (Print) | 1550-5499 |
Abstract
In this paper, we present a framework for automatically analyzing activities and interactions, and recognizing traffic states from surveillance video. Activities and interactions are firstly learned by Hierarchical Dirichlet Process (HDP) models based on low-level visual features. Based on the learning results, a Gaussian Process (GP) classifier is trained to classify the traffic states in online video. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification. Our framework couples the benefits of the generative models-HDP with the discriminant models-GP. We validate the proposed model by applying it to the analysis of the three standard video datasets over crowded traffic scenes and compare it with other baseline models. Experimental results demonstrate that our model is effective and efficient.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
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- BibTex
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Proceedings: 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015. Institute of Electrical and Electronics Engineers Inc., 2015. S. 166-174 7406380 (Proceedings of the IEEE International Conference on Computer Vision; Band 2015-February).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Video Event Recognition by Combining HDP and Gaussian Process
AU - Liao, Wentong
AU - Rosenhahn, Bodo
AU - Yang, Machael Ying
PY - 2015/2/11
Y1 - 2015/2/11
N2 - In this paper, we present a framework for automatically analyzing activities and interactions, and recognizing traffic states from surveillance video. Activities and interactions are firstly learned by Hierarchical Dirichlet Process (HDP) models based on low-level visual features. Based on the learning results, a Gaussian Process (GP) classifier is trained to classify the traffic states in online video. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification. Our framework couples the benefits of the generative models-HDP with the discriminant models-GP. We validate the proposed model by applying it to the analysis of the three standard video datasets over crowded traffic scenes and compare it with other baseline models. Experimental results demonstrate that our model is effective and efficient.
AB - In this paper, we present a framework for automatically analyzing activities and interactions, and recognizing traffic states from surveillance video. Activities and interactions are firstly learned by Hierarchical Dirichlet Process (HDP) models based on low-level visual features. Based on the learning results, a Gaussian Process (GP) classifier is trained to classify the traffic states in online video. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification. Our framework couples the benefits of the generative models-HDP with the discriminant models-GP. We validate the proposed model by applying it to the analysis of the three standard video datasets over crowded traffic scenes and compare it with other baseline models. Experimental results demonstrate that our model is effective and efficient.
KW - Analytical models
KW - Computational modeling
KW - Feature extraction
KW - Hidden Markov models
KW - Surveillance
KW - Training
KW - Vocabulary
UR - http://www.scopus.com/inward/record.url?scp=84962023197&partnerID=8YFLogxK
U2 - 10.1109/iccvw.2015.31
DO - 10.1109/iccvw.2015.31
M3 - Conference contribution
AN - SCOPUS:84962023197
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 166
EP - 174
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015
Y2 - 11 December 2015 through 18 December 2015
ER -