STIMULATE: Video Scene Segmentation and Classification Using Motion Information

视频序列是丰富的多模态信息源，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（在各个帧中反映）， 和 这些物体的运动（通过帧之间的变化显示）。 虽然人类可以快速地从不同模态所携带的信息中解释出嵌入的语义内容，但计算机对视频序列的理解仍处于初级阶段。 本项目的目标是发展新的理论和技术，用于视频序列中的场景分割和分类，这是视频理解的关键。这个竞技场的研究 在过去的几年里，它集中在文本、语音和图像信息的使用上。 拟议的研究探讨了使用运动和音频特征，这将提供重要的 免费信息 新的成果预计在一般理论的特征分析和分类，并在视频理解和场景分类的实用技术。 这些 新 发展将直接应用于 在多媒体数据库中的信息索引和检索、监控视频中的特殊事件的发现和跟踪、视频编辑和电影分层等方面。

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.