首页> 外文期刊>IEEE Transactions on Circuits and Systems for Video Technology >Object-based video abstraction for video surveillance systems
【24h】

Object-based video abstraction for video surveillance systems

机译:视频监控系统的基于对象的视频抽象

获取原文
获取原文并翻译 | 示例

摘要

Key frames are the subset of still images which best represent the content of a video sequence in an abstracted manner. In other words, video abstraction transforms an entire video clip to a small number of representative images. We present a scheme for object-based video abstraction facilitated by an efficient video-object segmentation (VOS) system. In such a framework, the concept of a "key frame" is replaced by that of a "key video-object plane (VOP)." In order to achieve an online object-based framework such as an object-based video surveillance system, it becomes essential that semantically meaningful video objects are directly accessed from video sequences. Moreover, the extraction of key VOPs needs to be automated and context dependent so that they maintain the important contents of the video while removing all redundancies. Once a VOP is extracted, the shape of the VOP needs to be well described. To this end, both region-based and contour-based shape descriptors are investigated, and the region-based descriptor is selected for the proposed system. The key VOPs are extracted in a sequential manner by successive comparison with the previously declared key VOP. Experimental results on the proposed online processing scheme combined with efficient VOS show the proposed integrated scheme generates desirable summarizations of surveillance videos.
机译:关键帧是静止图像的子集,它最能抽象地代表视频序列的内容。换句话说,视频抽象将整个视频剪辑转换为少量的代表性图像。我们提出了一种基于对象的视频抽象方案,该方案由有效的视频对象分割(VOS)系统促进。在这样的框架中,“关键帧”的概念被“关键视频对象平面(VOP)”的概念所代替。为了获得诸如基于对象的视频监视系统之类的基于在线对象的框架,从视频序列中直接访问语义上有意义的视频对象变得至关重要。此外,关键VOP的提取必须是自动化的并且取决于上下文,以便它们在删除所有冗余的同时保持视频的重要内容。一旦提取了VOP,就需要很好地描述VOP的形状。为此,研究了基于区域和基于轮廓的形状描述符,并为所提出的系统选择了基于区域的描述符。通过与先前声明的密钥VOP进行连续比较,以顺序方式提取密钥VOP。提出的在线处理方案结合有效的VOS的实验结果表明,提出的集成方案可生成所需的监视视频摘要。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号