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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. [ABSTRACT FROM AUTHOR]