Long-Range Spatio-Temporal Modeling of Video with Application to Fire Detection

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 normalmodel 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.