Background subtraction is a classic video processing task pervading in numerous visual applications such as video surveillance and traffic monitoring. Given the diversity and variability of real application scenes, an ideal background subtraction model should be robust to various scenarios. Even though deep-learning approaches have demonstrated unprecedented improvements, they often fail to generalize to unseen scenarios, thereby less suitable for extensive deployment. In this work, we propose to tackle cross-scene background subtraction via a two-phase framework that includes meta-knowledge learning and domain adaptation. Specifically, as we observe that meta-knowledge (i.e., scene-independent common knowledge) is the cornerstone for generalizing to unseen scenes, we draw on traditional frame differencing algorithms and design a deep difference network (DDN) to encode meta-knowledge especially temporal change knowledge from various cross-scene data (source domain) without intermittent foreground motion pattern. In addition, we explore a self-training domain adaptation strategy based on iterative evolution. With iteratively updated pseudo-labels, the DDN is continuously fine-tuned and evolves progressively toward unseen scenes (target domain) in an unsupervised fashion. Our framework could be easily deployed on unseen scenes without relying on their annotations. As evidenced by our experiments on the CDnet2014 dataset, it brings a significant improvement to background subtraction. Our method has a favorable processing speed (70 fps) and outperforms the best unsupervised algorithm and top supervised algorithm designed for unseen scenes by 9% and 3%, respectively.

Setting up a multidimensional network is an important problem in complex networks and has become a future development trend in the fields of biological gene networks, social networks and so on. A multidimensional network comprises connections and attributes. Community detection in heterogeneous dat asets in different dimensions is more difficult than that in a single network. Traditional methods for dealing with multidimensional networks are ineffective, because of using supervised information or applying strategies for adjusting the graph structure of a single network. In this paper, we propose a semi-supervised community detection method for multidimensional heterogeneous networks. First, we generate a single network by integrating the multidimensional heterogeneous networks. The robust semi-supervised link adjustment strategy is then iteratively applied to the single network to make full use of dynamic supervised information for adding or removing links based on node entropy. Experimental results are obtained by five real multidimensional social datasets. The results show that the proposed method can effectively integrate heterogeneous data. The average accuracy rate and standard mutual information were 90.50% and 93.99%, respectively, representing improvements of 28.97% and 35.06%, respectively, over existing methods.