Qualitative Analysis of Spatio-Temporal Event Detectors
Interest point detection is an established method to select relevent image regions. Such techniques use features like corners or edges, which are known to indicate regions likely to hold patterns of interest. Selection of such regions increases processing efficiency. For the recognition of motion, h...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.1108 http://figment.cse.usf.edu/~sfefilat/data/papers/MoBT8.20.pdf |
| Summary: | Interest point detection is an established method to select relevent image regions. Such techniques use features like corners or edges, which are known to indicate regions likely to hold patterns of interest. Selection of such regions increases processing efficiency. For the recognition of motion, however, such context-free methods are still very rare. Though there are numerous methods to find space-time volumes of motion in image sequences, most aim at finding just motion as a such, not volumes which are more promising for analysis than others. Therefore Laptev and Lindeberg (2005) generalized the Harris detector to the spatio-temporal domain. But the problem remains to evaluate what kind of motion is captured by a detector. For example, the detector of Laptev and Lindeberg should capture “corners” — like the original 2D-version of Harris and Stephens (1988) — but what does that mean for motion? Therefore we present an approach to visualize events which were selected by a spatio-temporal interest point detector. Since the analysis of single examples is not fruitful, we use clustering to analyze large quantities of space-time volumes selected by a detector. The resulting cluster centers are prototypical events, representing the types of events the detector responds to. Thus a qualitative yet statistically exhaustive analysis of detector properties is possible. 1 |
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