On space-time interest points
On space-time interest points
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DOI：
10.1007/s11263-005-1838-7
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发表时间：
2005-09-01
影响因子：
19.5
通讯作者：
Laptev, I
中科院分区：
文献类型：
--
作者：
Laptev, I
局部图像特征或兴趣点提供图像中模式的紧凑和抽象表示。该文将空间兴趣点的概念扩展到时空域中，展示了空间兴趣点的特征往往反映了感兴趣的事件，这些事件可以用于视频数据的紧凑表示以及时空事件的解释。为了检测时空事件，我们基于Harris和Forstner兴趣点算子的思想，检测图像在空间和时间上都具有显著局部变化的时空局部结构。我们通过在空间和时间尺度上最大化归一化时空拉普拉斯算子来估计检测事件的时空范围。为了表示检测到的事件，我们然后计算局部的、时空的、尺度不变的N-Jet，并根据其JET描述符对每个事件进行分类。对于人体运动分析的问题，我们说明了基于局部时空特征的视频表示如何能够在具有遮挡和动态杂乱背景的场景中检测行走的人。
Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for interpretation of spatio-temporal events.To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events, we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how a video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.