Detection of Flames Using Videos by Expectation Maximization and Flow Estimation Algorithm
Automatic flame detection using real time vision-based method has drawn potential significance in last decade. The very interesting dynamics of flames have motivated the use of motion estimators to distinguish fire from other types of motion. Since fire is a complex but unusual visual phenomenon, employs distinctive parameters such as color, motion, shape, growth, dynamic texture and smoke behavior, this paper proposes expectation maximization (EM) algorithm and flow estimation, enables parameter estimation in probabilistic models with incomplete data. The expectation maximization algorithm alternates between the steps of guessing a probability distribution over completions of missing data given the current model (known as the E-step) and then reestimating the model parameters using these completions (known as the M-step). Discrimination between fire and non-fire motion can be easily determined from the flow estimation. Our approach is capable of detecting fire reliably. Moreover it drastically reduces the false alarms.
Sasirekha.SP, ChinnuThomas, P. Dhivya, P.Sathyashrisharmila