An iterative-weighted-average algorithm for background modeling in surveillance video scenarios

An iterative-weighted-average algorithm for background modeling in surveillance video scenarios is proposed in this paper. Through allocating the pixel values at the same position in several consective frames in a sliding time window with different weight values during the iterative process, the influence of foreground pixel values on background modeling can be removed immediately without requiring large hardware memory. Furthermore, the algorithm we propose can also perform well in time-varying backgrounds such as the parking lot. Simulation result demonstrates that the proposed algorithm is capable of saving a large amount of coding bits, while maintaining the same computing complexity and SNR.