Extract foreground objects based on sparse model of spatiotemporal spectrum

In this paper, we present a novel foreground object detection method based on the sparse model of the spectrum of spatiotemporal DCT domain, which is robust for high dynamic scenes. First, we adopt the three-dimensional Discrete Cosine Transform (DCT) to calculate the spatiotemporal spectrum representation of the current frame. Then, identification of foreground pixels is formulated as the analysis of the sparse solution of an optimization problem, where foreground pixels correspond to an outlier of the sparse model. Finally, the background updating method is presented to adaptively update the dictionary of sparse model corresponding to background representation. The experimental results on four challenging video sequences show that the proposed method is more robust to high dynamic changes of scenes compared with four representative methods.