Smoke detection in video sequences based on dynamic texture using volume local binary patterns

Author:  Lin, G. H., Zhang, Y. M., Zhang, Q. X., Jia, Y., Xu, G., Wang, J. J.

Journal: Ksii Transactions on Internet and Information Systems

DOI: 10.3837/tiis.2017.11.019

Keywords: Smoke detection, Video sequences, Volume local binary pattern, Dynamic texture, Support vector machine, classification, motion, Computer Science, Telecommunications

Abstract:

In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.