Abstract
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 event-consistent sub-sequences, and their grouping into event-consistent
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 available (either as a result of the clustering process,
or given manually), these can be used to refine the event representation,
the associated distance measure, and accordingly the quality of the detection
and clustering process.
Click here for full paper
ZelnikIrani.CVPR2001.ps.zip
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(678K)
Full length sequence (54022K)
Clustering actions of a tennis player
Punch-Kick-Duck Sequence
Action Based Video
Synthesis
Raw Footage (punch-duck-kick).
Classifying dynamic textures
Questions and comments should be addressed to:
lihi@wisdom.weizmann.ac.il
