Background Modeling to Detect Foreground Objects Based on ANN and Spatio-Temporal Analysis

This paper presented an approach to building background model for moving object detection using unsupervised artificial neural network (ANN) without any prior knowledge about foreground objects. First, using local binary pattern (LBP) which is texture feature, builds a statistical Background Model using ANN, then, comparing the behavior of next incoming frame with model and decide each pixel whether is deviating from a model or not. And based on if method detects foreground objects then background model is updated to make this model adaptive. Also, spatial-temporal information has been exploited in this method to suppress sudden illumination variation and to suppress false foreground pixels. It was demonstrated and proved, by qualitative and quantitative metrics that the newly presented approach is adaptive, generic and can address all issues and challenges for background subtraction. To evaluate the performance of the presented approach this paper compared with recent approaches by using standard metrics and proved that presented method outperforms many existing recent approaches.