Video Stabilization and Text-Based Sentence Identification for Video-Based Answering Systems

Abstract

Recently, teaching using video clips is a simple and direct method instead of the text-based approaches. On the E-learning platform, students ask a question, and teachers answer the question using video clips especially the derivation of mathematical equations. Teachers write their answers down on an A4-size paper, use an APP to record the whole process, and upload the video clips to the platform. However, reading the sentences or equations from videos is very uncomfortable due to the paper movements when teacher wrote the sentences. In addition, teacher's hand frequently occludes the answers during the recording process. In the study, a video stabilizer with hand removal is proposed for comfortably reading. There are two key components: Video stabilization and sentence identification. Video stabilization estimates the smooth camera paths using SURF -based corner detection, hand part removal, image registration, and frame calibration. In sentence identification, sentences are segmented and extracted using contour detection and the convex hull generation algorithm. After the calibration process, users won't fell dizzy in reading sentences from calibrated and un-shaky videos. In addition, time stamps are marked at the beginning and the end of each sentence in the processed videos. When users click a specified sentence, a stabilized clip with good quality will be played.