Pattern Classification Techniques for Flame Detection in Videos Using Optical Flow Estimation

Vision based fire detection is potentially a useful technique. Vision-based detection is composed of the following three steps. Preprocessing is necessary to compensate for known sources of variability. Feature extraction is designed for the detection of a specific target. Classification algorithms use the computed features as input and make decision outputs regarding the target’s presence. Supervised machine learning based classification algorithms such as neural networks (NN) are systematically trained on a data set of features and ground truth. Since classical optical flow methods do not model the characteristics of fire motion two optical flow methods are specifically designed for the fire detection task: optimal mass transport models fire with dynamic texture, while a data-driven optical flow scheme models saturated flames. Then, characteristic features related to the flow magnitudes and directions are computed from the flow fields to discriminate between fire and non-fire motion.