Spatial-temporal structural and dynamics features for Video Fire Detection

Abstract

We present a new Video Fire Detection (VFD) system for surveillance applications in fire and security industries. The system consists of three modules: pixel-level processing to identify potential fire blobs, blob-based spatial-temporal feature extraction, and a Support Vector Machine (SVM) classifier. The proposed novel spatial-temporal features include a spatial-temporal structural feature and a spatial-temporal contour dynamics feature. The spatial-temporal structural features are extracted from an accumulated motion mask (AMM) and an accumulated intensity template (AIT), capturing the concentric ring structure of fire intensity. The spatial-temporal dynamics features are based on the Fourier descriptor of contours in space and time, capturing the dynamic properties of fire. These global blob-based features are more robust and effective in rejecting false alarms and nuisance sources than pixel-wise features. In addition, extraction of the spatial-temporal features is very efficient, and no tracking of blobs or contours is needed. We also present a new multi-spectrum fire video database for algorithm testing. We evaluate the effectiveness of the proposed features on fire detection on the video database and obtain very promising results.