An Effective Video Text Tracking Algorithm Based on SIFT Feature and Geometric Constraint

Abstract

Video text provides important clues for semantic-based video analysis, indexing and retrieval. And text tracking is performed to locate specific text information across video frames and enhance text segmentation and recognition over time. This paper presents a multilingual video text tracking algorithm based on the extraction and tracking of Scale Invariant Feature Transform (SIFT) features description through video frames. SIFT features are extracted from video frames to correspond the region of interests across frames. Meanwhile, a global matching method using geometric constraint is proposed to decrease false matches, which effectively improves the accuracy and stability of text tracking results. Based on the correct matches, the motion of text is estimated in adjacent frames and a match score of text is calculated to determine Text Change Boundary (TCB). Experimental results on a large number of video frames show that the proposed text tracking algorithm is robust to different text forms, including multilingual captions, credits, scene texts with shift, rotation and scale change, under complex backgrounds and light changing.