By exploiting the potential of deep learning, video compressive sensing (CS) has achieved tremendous improvement recently. Due to the video CS is mainly served for the fixed scene in real life. In this paper, we propose a novel video compressive sensing with a low-complexity region-of-interest (ROI) detection method (VCSL). The ROI is located by calculating the difference between the reference frame and the following frames in our framework, which is compact without introducing any additional neural networks and parameters. Subsequently, only the detected ROIs are sampled and transmitted, except for the frame that is regarded as the background. The final reconstructed sequence would be attained by combining the ROIs and the background. Compared to the state-of-the-art counterparts, extensive experimental results have demonstrated that our proposed methods achieve superior performance while tackling more complex sequences and using a lower sampling rate.