A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants

Smoke detection is an advance caution to the unforeseen great damage events. Therefore, it is required to identify the smoke in the course of initial stages for preventing fire events. A new technique is proposed to lessen the rate of incorrect alarm by identify the smoke and examine its distinctive texture attributes. Initially, the smoke-colored regions are segmented based on color at the YUV color locality. Then the tentative frame differencing is used to segment the candidate smoke region from the smoke-colored region. In the next phase, the candidate distinctive texture attributes in the smoke region are extracted using Co-occurrence of Hamming Distance based Local Binary pattern (CoHDLBP) and Co-occurrence of Local Binary pattern (CoLBP); these features include homogeneity, energy, correlation and contrast. Finally, the ELM classifier is proficient for the take-out features from the candidate smoke region, and then the decision has been taken with the assistance of a smoke alarm. Investigational outcomes proved that the suggested smoke recognition process executes better compared with all the usual smoke recognition methods by achieving better detection accuracy and processing time.