Text in videos contains rich semantic information, which is useful for content based video understanding and retrieval. Although a great number of state-of-the-art methods are proposed to detect text in images and videos, few works focus on spatiotemporal text localization in videos. In this paper, we present a spatiotemporal text localization method with an improved detection efficiency and performance. Concretely, a unified framework is proposed which consists of the sampling-and-recovery model (SaRM) and the divide-and-conquer model (DaCM). SaRM aims at exploiting the temporal redundancy of text to increase the detection efficiency for videos. DaCM is designed to efficiently localize the text in spatiotemporal domain simultaneously. Besides, we construct a challenging video overlaid text dataset named UCAS-STLData, which contains 57070 frames with spatiotemporal ground truths. In the experiments, we comprehensively evaluate the proposed method on the publicly available overlaid text datasets and UCAS-STLData. A slight performance improvement is achieved compared with the state-of-the-art methods for spatiotemporal text localization, with a significant efficiency improvement.