Several low-rank matrix decomposition-based approaches have been proposed for moving object detection in recent years. However

most of these methods use the nuclear norm to substitute rank functions for optimization. As a result

the precision of background recovery is relatively low. Another problem is the failure of these methods to use prior knowledge of the regional continuity of foreground objects

which is important information for object detection. To solve these issues

we propose a novel object detection method that combines the weighted non-convex nuclear norm and the regional continuity of the foreground object. The new object detection model is designed on the basis of the robust principal component analysis. The proposed model uses the weighted non-convex nuclear norm to replace the traditional nuclear norm for low-rank constraints. Furthermore

the prior knowledge of the regional continuity of the foreground object is added to restrain the clustered objects. By using this model

the recovered low-rank matrix becomes the background image matrix

and the large sparse noise matrix becomes the foreground object matrix. Experiments demonstrate that the proposed method outperforms other low-rank decomposition-based approaches in both the simulated data and real sequences. Specifically

the proposed methodology shows an increased projected target detection performance that is 25% and 2% greater than that of RPCA and DECOLOR. With respect to the two approaches

the proposed method reduces background recovery errors by about 0.5 and 0.2. The non-convex relaxation of rank functions possesses better properties than the convex one in approximating matrix ranks

which is useful in restoring background images in motion object detection. The regional continuity of foreground objects allows the efficient exclusion of scattered outliers to enhance the effect of the objects detected. Therefore

this method can detect moving targets accurately in complex scenes

such as those with dynamic backgrounds and illumination-changing scenarios. However

the proposed method is not ideal for multi-object detection in small areas because of regional continuity requirements.