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Interest point detection and scale selection in space-time
Interest point detection and scale selection
in space-time
Ivan Laptev and Tony Lindeberg
Computational Vision and Active Perception Laboratory (CVAP)
Dept. of Numerical Analysis and Computing Science
KTH, S-100 44 Stockholm, Sweden
Abstract. Several types of interest point detectors have been proposed
for spatial images. This paper investigates how this notion can be gener-
alised to the detection of interesting events in space-time data. Moreover,
we develop a mechanism for spatio-temporal scale selection and detect
events at scales corresponding to their extent in both space and time.
To detect spatio-temporal events, we build on the idea of the Harris
and Fo?rstner interest point operators and detect regions in space-time
where the image structures have significant local variations in both space
and time. In this way, events that correspond to curved space-time struc-
tures are emphasised, while structures with locally constant motion are
disregarded.
To construct this operator, we start from a multi-scale windowed sec-
ond moment matrix in space-time, and combine the determinant and
the trace in a similar way as for the spatial Harris operator. All space-
time maxima of this operator are then adapted to characteristic scales by
maximising a scale-normalised space-time Laplacian operator over both
spatial scales and temporal scales. The motivation for performing tem-
poral scale selection as a complement to previous approaches of spatial
scale selection is to be able to robustly capture spatio-temporal events of
different temporal extent. It is shown that the resulting approach is truly
scale invariant with respect to both spatial scales and temporal scales.
The proposed concept is tested on synthetic and real image sequences.
It is shown that the operator responds to distinct and stable points in
space-time that often correspond to interesting events. The potential
applications of the method are discussed.
1 Introduction
Analysing and interpreting video is a g
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