Comparative Study on Early Smoke Detection Based on Video of Forest Fire Front and Side Shooting
Traditional smoke detection uses contact detection methods such as smoke, temperature, and light, which have limitations in space and scenes. Video-based fire detection and recognition is the current research hotspot, and fire monitoring and recognition based on multi-rotor aircraft is a new research direction. This paper uses the background blur characteristics of smoke to compare the two surveillance videos. First, the background of the video is extracted by establishing a background model, and then the frame images of the video are decomposed and reconstructed by two-dimensional discrete wavelet transform to complete the recognition of smoke. The experimental result is that the average recognition rate of smoke from remote side shots is 86.83%. The average smoke recognition rate of the aircraft is 96.94%.