Recognizing Human Actions: A Local SVM Approach
Christian Schuldt, Ivan Laptev and Barbara Caputo
In Proc. ICPR 2004, Cambridge, UK.
Local space-time features capture local events in video
and can be adapted to the size, the frequency and the velocity
of moving patterns.
In this paper we demonstrate how such features can be used
for recognizing complex motion patterns.
We construct video representations in terms of local space-time
features and integrate such representations with SVM classification
schemes for recognition.
For the purpose of evaluation we introduce a new video database
containing 2391 sequences of six human actions performed by 25 people
in four different scenarios.
The presented results of action recognition justify the proposed
method and demonstrate its advantage compared to other relative
approaches for action recognition.
Recognition of human actions
(Local descriptors for spatio-temporal recognition)
(Velocity adaptation of space-time interest points)
(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)