Event-Based Analysis of Video

Dynamic events can be regarded as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences (possibly of different lengths) based on their behavioral content. This measure is non-parametric and can thus handle a wide range of dynamic events. We use this measure for isolating and clustering events within long continuous video sequences. This is done without prior knowledge of the types of events, their models, or their temporal extent. An outcome of such a clustering process is a temporal segmentation of long video sequences into eventconsistent sub-sequences, and their grouping into eventconsistent clusters. Our event representation and associated distance measure can also be used for event-based indexing into long video sequences, even when only one short example-clip is available. However, when multiple example-clips of the same event are availabl...

• Type: Conference
• Year: 2001
• Where: CVPR
• Authors: Lihi Zelnik-Manor, Michal Irani
• Keywords: Associated Distance Measure, Clustering Process, Computer Vision, CVPR 2001, Long Video Sequences, Multiple Temporal Scales, Statistical Distance Measure