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GAUSSIAN PROCESS FOR ACTIVITY MODELING AND ANOMALY DETECTION
GAUSSIAN PROCESS FOR ACTIVITY MODELING AND ANOMALY DETECTION
- Source :
- Proceeding of ISPRS Geospatial Week 2015, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 2, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol II-3-W5, Pp 467-474 (2015)
- Publication Year :
- 2015
- Publisher :
- Copernicus GmbH, 2015.
-
Abstract
- Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set.
- Subjects :
- lcsh:Applied optics. Photonics
Dewey Decimal Classification::500 | Naturwissenschaften::550 | Geowissenschaften
Machine learning
computer.software_genre
lcsh:Technology
symbols.namesake
Robustness (computer science)
ddc:550
Gaussian Process regression
Hidden Markov model
Gaussian process
Konferenzschrift
Activity modeling
Mathematics
Ground truth
Anomaly detecti
lcsh:T
business.industry
Probabilistic logic
Nonparametric statistics
lcsh:TA1501-1820
lcsh:TA1-2040
Parametric model
symbols
Anomaly detection
Artificial intelligence
Data mining
lcsh:Engineering (General). Civil engineering (General)
business
computer
Subjects
Details
- ISSN :
- 21949050
- Database :
- OpenAIRE
- Journal :
- ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
- Accession number :
- edsair.doi.dedup.....6074a2f571bc5dd8e85bccf6fae8c668
- Full Text :
- https://doi.org/10.5194/isprsannals-ii-3-w5-467-2015