The paper proposes a non-parametric moving target detection algorithm based on sparse representation residuals error.In order to achieve precise motion target detection

we assume that the foreground change can be seen as sparse residuals compared with the static background.First of all

we use first

n

frames of the video to initialize the sparse representation dictionary.It will be applied to reconstruct the subsequent frame

extract frame residuals of every image

and then extract binary foreground images combining with the pixel-based global threshold value matrix.Furthermore

we remove ghost area on the basis of the foreground and edge regions.Finally

using the in

cremental PCA(Principal Component Analysis)and the idea of keep and update

we renew the above background model.A set of experiments are conducted on the shadow sets of changedetection.net using global update and residual error calculation method

and the result shows that the algorithm is an effective and efficient way to adapt to changes in the shadow of a static scene because of the changes of light.What is more

as to the small amplitude changes of the static scene and camera shake problems

it can also be a good solution.