  With only bounding-box annotations in the spatial domain, existing video
scene text detection (VSTD) benchmarks lack temporal relation of text instances
among video frames, which hinders the development of video text-related
applications. In this paper, we systematically introduce a new large-scale
benchmark, named as STVText4, a well-designed spatial-temporal detection metric
(STDM), and a novel clustering-based baseline method, referred to as Temporal
Clustering (TC). STVText4 opens a challenging yet promising direction of VSTD,
termed as ST-VSTD, which targets at simultaneously detecting video scene texts
in both spatial and temporal domains. STVText4 contains more than 1.4 million
text instances from 161,347 video frames of 106 videos, where each instance is
annotated with not only spatial bounding box and temporal range but also four
intrinsic attributes, including legibility, density, scale, and lifecycle, to
facilitate the community. With continuous propagation of identical texts in the
video sequence, TC can accurately output the spatial quadrilateral and temporal
range of the texts, which sets a strong baseline for ST-VSTD. Experiments
demonstrate the efficacy of our method and the great academic and practical
value of the STVText4. The dataset and code will be available soon.
