Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 19-26 |
| Seitenumfang | 8 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 4 |
| Ausgabenummer | 1W1 |
| Publikationsstatus | Veröffentlicht - 30 Mai 2017 |
| Veranstaltung | ISPRS Hannover Workshop 2017: HRIGI - High-Resolution Earth Imaging for Geospatial Information, CMRT - City Models, Roads and Traffic, ISA - Image Sequence Analysis, EuroCOW - European Calibration and Orientation Workshop - Hannover, Hannover, Deutschland Dauer: 6 Juni 2017 → 9 Juni 2017 |
Abstract
With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
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 4, Nr. 1W1, 30.05.2017, S. 19-26.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - SECURITY EVENT RECOGNITION for VISUAL SURVEILLANCE
AU - Liao, Wentong
AU - Yang, Chun
AU - Ying Yang, M.
AU - Rosenhahn, Bodo
N1 - Funding information: The work is funded by DFG (German Research Foundation) YA 351/2-1. The authors gratefully acknowledge the support.
PY - 2017/5/30
Y1 - 2017/5/30
N2 - With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
AB - With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.
KW - Computer Vision
KW - Convolutional Neural Network
KW - Event Recognition
KW - Video Surveillance
UR - http://www.scopus.com/inward/record.url?scp=85044528030&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-IV-1-W1-19-2017
DO - 10.5194/isprs-annals-IV-1-W1-19-2017
M3 - Conference article
AN - SCOPUS:85044528030
VL - 4
SP - 19
EP - 26
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 - 1W1
T2 - ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017
Y2 - 6 June 2017 through 9 June 2017
ER -