Framework for detecting hazardous events

Using video analysis for detecting hazardous events such as fire/smoke activity, impending threats, or suspicious behaviors has spurred new research for security concerns. To make such detection reliable, researchers must overcome difficulties such as classification by the importance of consequences, imbalances of positive and negative data, environmental factors, and variation in camera capabilities. This paper puts forward a general framework for hazardous event detection which includes spatial-temporal feature extraction, statistical-based classification for biased data and calibration for environmental change. At the current stage of development, the framework can work effectively for detecting hazardous events like fire/smoke from video sequences.