Background Subtraction with Dirichlet process Gaussian Mixture Model (DP-GMM) for Motion Detection

Video analysis often starts with background subtraction. This problem This problem is often loomed in two steps: Per-pixel background model followed by regulation scheme. A background model allows it to distinguished on Per-pixel basis from foreground, though the regularization combines information from adjacent pixels.Dirichlet process Gaussian mixture models is a method, which are used to approximate per-pixel background distributions followed by probabilistic regularization. Per pixel modes are automatically count by using non-parametric Bayesian method, avoiding over-/under- fitting. We implemented this method using FPGA and also compare the results with different methods like Background subtraction; Frame difference and Neural map and shows how this method is superior then previous methods.