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 incremental 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.
蒋建国, 金玉龙, 齐美彬, 詹曙. 基于稀疏表达残差的自然场景运动目标检测[J]. 电子学报, 2015, 43(9): 1738-1744.
JIANG Jian-guo, JIN Yu-long, QI Mei-bin, ZHAN Shu. Moving Target Detection in Natural Scene Based on Sparse Representation of Residuals. Chinese Journal of Electronics, 2015, 43(9): 1738-1744.
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2015, Vol. 43 
