Whiteboard Video Summarization via Spatio-Temporal Conflict Minimization

Lecture videos are a valuable resource for students, and thanks to online sources they have become widely available. The ability to find videos based on their content could make them even more useful. Methods for automatic extraction of this content reduce the amount of manual effort required to make indexing and retrieval of such videos possible. We present a method that generates static image summaries of handwritten whiteboard content from lecture videos recorded with still cameras. We generate a spatio-temporal index for the handwritten content in the video, and we use it for temporal segmentation by detecting and removing conflicts between content regions. Resulting segments are used to produce key-frame based summaries. Our method has been tested on a video collection showing promising results for automatic lecture video summarization with good compression ratios and 96.28% recall of connected components in test videos.