  Slow Feature Analysis (SFA) extracts slowly varying features from a quickly
varying input signal. It has been successfully applied to modeling the visual
receptive fields of the cortical neurons. Sufficient experimental results in
neuroscience suggest that the temporal slowness principle is a general learning
principle in visual perception. In this paper, we introduce the SFA framework
to the problem of human action recognition by incorporating the discriminative
information with SFA learning and considering the spatial relationship of body
parts. In particular, we consider four kinds of SFA learning strategies,
including the original unsupervised SFA (U-SFA), the supervised SFA (S-SFA),
the discriminative SFA (D-SFA), and the spatial discriminative SFA (SD-SFA), to
extract slow feature functions from a large amount of training cuboids which
are obtained by random sampling in motion boundaries. Afterward, to represent
action sequences, the squared first order temporal derivatives are accumulated
over all transformed cuboids into one feature vector, which is termed the
Accumulated Squared Derivative (ASD) feature. The ASD feature encodes the
statistical distribution of slow features in an action sequence. Finally, a
linear support vector machine (SVM) is trained to classify actions represented
by ASD features. We conduct extensive experiments, including two sets of
control experiments, two sets of large scale experiments on the KTH and
Weizmann databases, and two sets of experiments on the CASIA and UT-interaction
databases, to demonstrate the effectiveness of SFA for human action
recognition.
