In this paper, we propose a new method for background subtraction in thermal videos by improving the running Gaussian average technique (RGA). First, we propose a new background modeling even in the presence of moving objects in scene using region-based robust principle component analysis (RPCA). To enhance the performance or reduce computation cost of the RGA, we incorporate selectivity and random spatial subsampling techniques into background updating scheme. In addition, we also propose a new technique to deal with intensity sudden change problem by detecting corrupted frame based on skewness value of histogram followed by intensity enhancement using histogram matching method. Finally, we reduce number of ROI regions for human detection step by using a candidate extraction using morphology operator. Experiment results with our thermal database confirm that the proposed method significantly outperforms the baseline RGA and frame difference methods. Specially, the recall rate, precision rate and F value of the proposed method are 82.02%, 75.08% and 73.20% in comparison to 76.12%, 42.80% and 39.64%, of the baseline RGA, respectively.