In recent years, the amount of streaming video has grown rapidly on the Web. Often, retrieving these streaming videos
offers the challenge of indexing and analyzing the media in real time because the streams must be treated as effectively
infinite in length, thus precluding offline processing. Generally speaking, captions are important semantic clues for video
indexing and retrieval. However, existing caption detection methods often have difficulties to make real-time detection
for streaming video, and few of them concern on the differentiation of captions from scene texts and scrolling texts. In
general, these texts have different roles in streaming video retrieval. To overcome these difficulties, this paper proposes a
novel approach which explores the inter-frame correlation analysis and wavelet-domain modeling for real-time caption
detection in streaming video. In our approach, the inter-frame correlation information is used to distinguish caption texts
from scene texts and scrolling texts. Moreover, wavelet-domain Generalized Gaussian Models (GGMs) are utilized to
automatically remove non-text regions from each frame and only keep caption regions for further processing.
Experiment results show that our approach is able to offer real-time caption detection with high recall and low false
alarm rate, and also can effectively discern caption texts from the other texts even in low resolutions.