Long-Range spatio-temporal modeling of video with application to fire detection

Authors:
Avinash Ravichandran;Stefano Soatto
Affiliations:
Vision Lab, University of California, Los Angeles, Los Angeles, CA;Vision Lab, University of California, Los Angeles, Los Angeles, CA
Venue:
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Year:
2012

Citing 7
Cited 0

Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Vision Based Fire Detection

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
What Do the Sun and the Sky Tell Us About the Camera?

International Journal of Computer Vision
Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Exploratory analysis of time-lapse imagery with fast subset PCA

WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Multivariate online kernel density estimation with Gaussian kernels

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a methodology for modeling backgrounds subject to significant variability over time-scales ranging from days to years, where the events of interest exhibit subtle variability relative to the normal mode. The motivating application is fire monitoring from remote stations, where illumination changes spanning the day and the season, meteorological phenomena resembling smoke, and the absence of sufficient training data for the two classes make out-of-the-box classification algorithms ineffective. We exploit low-level descriptors, incorporate explicit modeling of nuisance variability, and learn the residual normal-model variability. Our algorithm achieves state-of-the-art performance not only compared to other anomaly detection schemes, but also compared to human performance, both for untrained and trained operators.