Local Descriptors for Spatio-Temporal Recognition
Ivan Laptev and Tony Lindeberg
ECCV Workshop on Spatial Coherence for Visual Motion Analysis
Abstract
This paper presents and investigates a set of local space-time descriptors
for representing and recognizing motion patterns in video.
Following the idea of local features in the spatial domain,
we use the notion of space-time interest points and represent
video data in terms of local space-time events.
To describe such events, we define several types of image descriptors over
local spatio-temporal neighborhoods and evaluate these
descriptors in the context of recognizing human activities.
In particular, we compare motion representations in terms of
spatio-temporal jets, position dependent histograms,
position independent histograms, and
principal component analysis computed for either
spatio-temporal gradients or optic flow.
An experimental evaluation on a video database with human actions
shows that high classification performance can be achieved,
and that there is a clear advantage of using
local position dependent histograms, consistent with
previously reported findings regarding spatial recognition.
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Related projects:
Recognition of human actions
Related publications:
(Velocity adaptation of space-time interest points)
(Recognizing Human Actions: a Local SVM Approach)
(Space-time interest points)
(Interest point detection and scale selection in space-time)
(Velocity-adaptation of spatio-temporal receptive fields for
direct recognition of activities: An experimental study)
(Monograph on scale-space theory)
Ivan Laptev