Rule-Based Grass Biomass Classification for Roadside Fire Risk Assessment

Abstract

Roadside grass fire is a major hazard to the security of drivers and vehicles. However, automatic assessment of roadside grass fire risk has not been fully investigated. This paper presents an approach, for the first time to our best knowledge, that automatically estimates and classifies grass biomass for determining the fire risk level of roadside grasses from video frames. A major novelty is automatic measurement of grass coverage and height for predicting the biomass. For a sampling grass region, the approach performs two-level grass segmentation using class-specific neural networks. The brown grass coverage is then calculated and an algorithm is proposed that uses continuously connected vertical grass pixels to estimate the grass height. Based on brown grass coverage and grass height, a set of threshold based rules are designed to classify grasses into low, medium or high risk. Experiments on a challenging real-world dataset demonstrate promising results of our approach.