A neural adaptive level set method for wildland forest fire tracking

Tracking of smoke and fire in videos can provide helpful regional measures to evaluate precisely damages caused by fires. In security applications, real-time video segmentation of both fire and smoke regions represents a crucial operation to avoid disaster. In this paper, we propose a robust tracking method for fire regions in forest wildfire videos using neural pixel classification approach combined with a non-linear adaptive level set method based on the Bayesian rule. Firstly, an estimation function is built with chromatic and statistical features using linear discriminant analysis and a trained multilayer neural network in order to get a preliminary fire localisation in each frame. This function is used to compute initial curve and the level set evolution parameters providing fast refined fire segmentation in each processed frame. The experimental results of the proposed method prove its accuracy and robustness when tested on different varieties of wildfire-smoke scenarios.