Augmented 3-D Keyframe Extraction for Surveillance Videos

Abstract

In surveillance applications, keyframes are usually used to summarize important video content. However, traditional keyframe extracting methods select video frames as keyframes directly from the input video, and the information in these selected video frames is scattered and the users cannot perceive how the events happened in the original video easily. In this paper, we propose a new type of keyframe (called an “augmented 3-D keyframe”) that is a more meaningful and compact keyframe, augmented with representative objects, important contents (human faces, license plates, and so on), motion information (including trajectories and simple movement situations), and some marks of the moving objects extracted from a surveillance video clip captured by a static camera. The innovation is that we include simple 3-D geometric information to generate this augmented 3-D keyframe without reconstructing a complete 3-D scene. Testing results of the proposed augmented 3-D keyframe are presented and compared with results obtained by the traditional keyframe approach.