Every year, a large number of wildfires all over the world burn forested lands, causing adverse ecological, economic, and social impacts. Beyond taking precautionary measures, early warning and immediate response are the only ways to avoid great losses. The main challenge in video-based fire detection lies in the modeling of the chaotic and complex nature of the fire phenomenon and the large variations of both flame and smoke appearance in video.
The identification of smoke, especially in its early stage, is one of the key challenges of video-based fire detection systems. VCL has a strong expertise in developing AI algorithms for both flame and smoke detection in Video Surveillance Applications. The video sequence below demonstrates the efficiency of our smoke detection algorithm in a real fire incident. Our algorithm can detect smoke in its early stage despite its small size, the long distance from the camera, as well as the low resolution and quality of the video captured by a surveillance camera.
- P. Barmpoutis, K. Dimitropoulos, K. Kaza, N. Grammalidis, “Fire Detection from Images using Faster R-CNN and Multidimensional Texture Analysis”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), Brighton, UK, 12-17 May 2019.
- K. Dimitropoulos, P. Barmpoutis, A. Kitsikidis and N. Grammalidis, “Classification of Multidimensional Time-Evolving Data using Histograms of Grassmannian Points”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 28, no. 4, pp. 892 – 905, April 2018.
- K. Dimitropoulos, P. Barmpoutis and N. Grammalidis, “Higher Order Linear Dynamical Systems for Smoke Detection in Video Surveillance Applications”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), vol. 27, no. 5, pp. 1143 – 1154, May 2017.
- K. Dimitropoulos, P. Barmpoutis and N. Grammalidis, “Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Vol. 25, No. 2, pp. 339-351, February 2015.