STIMULATE: Video Scene Segmentation and Classification Using Motion Information

视频序列是丰富的多模式信息源，包括语音、文本、音频(非语音部分)、成像对象的颜色图案和形状(反映在各个帧中)以及这些对象的运动(通过帧之间的变化来揭示)。虽然人类可以从不同形式携带的信息中快速理解嵌入的语义内容，但计算机对视频序列的理解仍处于原始阶段。本项目的目标是开发新的理论和技术，用于视频序列中的场景分割和分类，这是视频理解的关键。在过去的几年里，这一领域的研究主要集中在文本、语音和图像信息的使用上。这项拟议的研究探索了运动和音频特征的使用，这将提供重要的补充信息。预计在特征分析和分类的一般理论以及视频理解和场景分类的实用技术方面都会有新的结果。这些新的发展将直接应用于多媒体数据库中的信息索引和检索、监视视频中的特殊事件的发现和跟踪、视频编辑和电影分层等。

A video sequence is a rich multimodal information source, including speech, text, audio (non-speech portion), color patterns and shapes of imaged objects (reflected in individual frames), and motion of these objects (revealed by changes between frames). Although the human being can quickly interpret the embedded semantic content from the information carried by different modalities, computer understanding of a video sequence is still in a primitive stage. The aim of this project is to develop new theory and techniques for scene segmentation and classification in a video sequence, which is key to video understanding. Research in this arena has in the past several years focused on the use of text, speech and image information. The proposed research explores the use of motion and audio characteristics, which will provide important complimentary information. New results are anticipated both in the general theory of feature analysis and classification, and in practical techniques for video understanding and scene classification. These new developments will have direct applications in information indexing and retrieval in multimedia databases, spotting and tracking of special events in surveillance video, video editing and movie stratification, etc.