Patent ID: 8599255

Claim:
A video surveillance system based on Gaussian mixture modeling with a two-type learning rate control scheme comprising: a processor configured to execute at least three processing modules, stored in an non-transitory storage medium, of: a background model maintenance module that constructs a background model as its module output from an image frame input, based on the Gaussian mixture modeling with a two-type learning rate control scheme; a foreground pixel identification module that marks a plurality of pixels of a current image frame of the plurality of image frames as foreground or background, and generates a foreground map as its output; and a high-level information extraction module that classifies the foreground pixels into a plurality of object level types via foreground object analysis; and at least two links of: an input link of the plurality of image frames; and a feedback link of high-level information that is generated by the high-level information extraction module and transmitted to the background model maintenance module, wherein the background model maintenance module being operated to perform steps of: receiving one of the plurality of image frames It; matching each pixel intensity It,x to one of N Gaussian models of a corresponding Gaussian mixture model, based on a weight-based model matching rule, l ⁡ ( t , x ) = arg ⁢ ⁢ min n = 1 , ⁢ … ⁢ , N ⁢ ⁢ d t , x , n , subject ⁢ ⁢ to d t , x , n = { - w t - 1 , x , n if ⁢ ⁢  I t , x - μ t - 1 , x , n  ≤ T σ ⁢ σ t - 1 , x , n inf otherwise , where l(t,x) indexes the best matched Gaussian model, if existing, for I 1,x , the three parameters, μ t-1,x,n , σ t-1,x,n , and w t-1,x,n , denote the mean, standard deviation and mixture weight, respectively, of the nth Gaussian distribution of the Gaussian mixture model, and T g is a given threshold; applying the two-type learning rate control scheme to the updating of the Gaussian mixture model, wherein two independent learning rates, ρ t,x and η t,x , are adopted by the iterative updating rules of the Gaussian parameters (μ and σ) and of the mixture weight (w), respectively, for the Gaussian mixture model of the location x at the current time instance t; and producing the Gaussian mixture background model at the current time instance t as the module output.