A Text Tracking Method using Maximally Stable Extremal Regions and Speeded up Robust Features

Abstract

Content-based image retrieval is an active research area because of vast applications of image and video collections. Text embedded in video can provide significant contribution in identifying the contents of the multimedia data and facilitate the process of video indexing, retrieval and analysis. Text tracking is a vital part of text extraction process. It can speed up the text extraction process and also improves the text localization accuracy for videos. This paper proposes a novel approach for text tracking in videos. This method experimentally proposes Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Features (SURF) descriptors for text tracking in videos. MSER is used for interest point detection as Extremal regions are affine invariant, so it is a suitable option for text tracking which can undergo scale, rotation and translation changes. SURF is chosen as a feature descriptor because of its scale and rotation invariance, speed and robustness. Proposed technique is testified on two datasets; the first is designed to test the tracking methodology as part of this research and second is publicly available Youtube Video Text(YVT) dataset. Succeeding analysis on diverse datasets endures verification to the fact that the projected technique demonstrates visible improvements to text tracking in videos