Foreground detection is a key research area in the field of video surveillance. The local binary similarity segmenter (LOBSTER)algorithm combines the visual background extractor (ViBe)algorithm with the local binary similarity patterns (LBSP)feature

which obtains excellent detection performance in general scenes.However

it has poor adaptability and high detection noise in the dynamic background. An improved LOBSTER algorithm is proposed to solve the aforementioned problems. The LBSP value of each pixel is calculated at the initialization stage of the model. The gray and LBSP values of the pixel are then added to each pixel of the color background model and LBSP background models

respectively

which enhances the description of the background model. The standard deviation

which is calculated in the neighborhood of each pixel

is utilized as a measurable index of the complexity of the pixel at the pixel classification stage. Adaptively adjusting the classification threshold in the color and LBSP background models can lower the noise in the foreground according to the background complexity. The conservative update strategy is still used in the improved LOBSTER algorithm to update the LOBSTER background model at the model updating stage.When a pixel is determined as the background

the pixel update is adopted as its own background model. If the background complexity of the pixel is smaller than a certain threshold

then the pixel is also added to the background model of the neighborhood by the probability of 1/

wherein the general value of is 16. If the background complexity is larger than a certain threshold

a new pixel is randomly selected in the pixel neighborhood which is classified as a background pixel. The selected pixel is then added to its own background model by the probability of 1/. The adaptability in the dynamic background is improved by adaptively updating the model strategy. Many qualitative analysis and quantitative calculations are presented in the ChangeDetection database for the improved LOBSTER algorithm in this study. The noise in the foreground image of the improved algorithm is less than that of the ViBe and LOBSTER algorithms. The value of the improved algorithm is higher by 0.736% to 7.56% than the ViBe algorithm and higher by approximately 0.77% to 12.47% than the LOBSTER algorithm in terms of the PCC index. The value of the improved LOBSTER algorithm is less than 1% of the ViBe and LOBSTER algorithms in terms of the FPC index. Simulation results show that the improved LOBSTER algorithm performs better than the conventional ViBe model and LOBSTER algorithm in dynamic conditions.Thus

our method has a higher accurate rate and stronger robustness in foreground detection.