Video object segmentation has been extensively investigated in computer vision recently because of its wide range of applications. The key factor of the segmentation is the construction of the spatiotemporal coherence. Inaccurate motion approximation as a measurement of the coherence usually leads to an inaccurate segmentation result. To obtain an accurate segmentation result, a low-rank sparse (LRS)-based approach is proposed. Regarding each superpixel as an element, this algorithm has a good segmentation accuracy compared with other pixel-level algorithms. Each element can be represented by the sparse linear combinations of dictionary templates, and this algorithm capitalises on the inherent low-rank structure of representations that are learnt jointly. The represented coefficients construct an affinity matrix which measures the elements' similarity between the current frame and the templates in the dictionary. For video object segmentation, a principled spatiotemporal objective function that uses LRS saliency term to propagate information between frames. Furthermore, an online parameter updating scheme is proposed to enhance the system's robustness. The online model propagates information forward without the need to access future frames. Evaluations on many challenging sequences demonstrate that the authors' approach outperforms the state-of-the-art methods in terms of object segmentation accuracy.