Improvement of a video smoke detection based on accumulative motion orientation model

Early fire-alarming is very important to avoid serious human being and materials losses. The traditional sensor-based methods can detect fire when the situation already has been dangerous. The video-based smoke detection can overcome these drawbacks. This paper proposes improvements of Yuan's video-based smoke detection, which employs accumulative motion orientation to detect smoke. In the proposed improvements, optimal thresholds for motion and chrominance detection are established and isolated noisy blocks are eliminated. The motion detection threshold is experimentally determined, and the chrominance detection thresholds are deduced from observation and testing of many videos with or without smoke. The elimination of isolated noisy blocks is achieved using the connected component labeling algorithm, which allows only processing the smoke regions, reducing the computational cost. Experimental results show that the proposed scheme increase the accuracy of the smoke detection and reduce the computation time.