Abstract:

In view that the existing background modeling method based on local binary similarity pattern (LBSP) is very vulnerable to external environment changes,such as dynamic background,illumination changes,camera shaking and so on,based on fusing pixel texture information with intensity information,we propose an adaptive hybrid model for moving object detecting.First,we use the texture descriptor of each pixel, named multi-channel adaptive local binary similarity pattern (LBSP) information,combined with intensity information,to build a hybrid background model.We then classify the current pixels according to the comparison results between the current pixel and the corresponding hybrid background model,and update the background model with the random updating mechanism.Experimental results show that the proposed method cannot only achieve good results in an ideal outside environment,but also effectively reduce the interference caused by complicated external environment conditions such as dynamic background,illumination changing and camera shaking,thus achieving  better detection results.

Key words: moving object detection, texture background model, adaptive LBSP, temporal background model, random update mechanism