Video synopsis is an effective technique for surveillance video browsing and storage. However, most of the existing video synopsis approaches are not suitable for complex situations, especially crowded scenes. This is because these approaches heavily depend on the preprocessing results of foreground segmentation and multiple objects tracking, but the preprocessing techniques usually achieve poor performance in crowded scenes. To address this problem, we propose a comprehensive video synopsis approach which can be applied to scenes with drastically varying crowdedness. The proposed approach differs significantly from the existing methods, and has several appealing properties. First, we propose to detect the crowdedness of a given video, then, extract object tubes in sparse periods and extract video clips in crowded periods, respectively. Through such a solution, the poor performance of preprocessing techniques in crowded scenes can be avoided by extracting the whole video frames. Second, we propose a group-partition algorithm which can discovers the relationships among moving objects and alleviates several segmentation and tracking errors. Third, a group-based greedy optimization algorithm is proposed to automatically determine the length of a synopsis video. Besides, we present extensive experiments that demonstrate the effectiveness and efficiency of the proposed approach.