Dynamic background subtraction using moments

Detection of moving objects is known to be a critical first step in many vision-based applications such as video surveillance. Background Subtraction Algorithms (BSA) offer a solution to detect all foreground pixels in a frame by comparing them with a background model. However, this is still a very challenging task especially when dynamic scenes are involved (camera jitter, noise, etc). In this paper, we present a new method for dynamic background subtraction operation based on invariant moments (Hu Set). Each pixel is modeled as a set of moments calculated from its neighborhood and stored using codebook construction. Experimental results on a set of outdoor scenes show that our method outperforms traditional BSA.