Fire Detection in Videos of Violent Crowds Acquired with Handheld Devices

Our paper proposes a novel method for fire detection in riot videos acquired with handheld cameras and smart-phones. This is a typical example of computer vision in the wild, where we have no control over the data acquisition process, and the quality of the video data varies considerably.

We propose a novel spatial model for fire, based on Gaussian mixtures and on color adjacency in the visible spectrum of incandescence. We demonstrate that using this spatial model in concert with motion cues leads to highly accurate results for fire detection in noisy, complex scenes of violent crowds.