  Understanding actions and gestures in video streams requires temporal
reasoning of the spatial content from different time instants, i.e.,
spatiotemporal (ST) modeling. In this survey paper, we have made a comparative
analysis of different ST modeling techniques for action and gecture recognition
tasks. Since Convolutional Neural Networks (CNNs) are proved to be an effective
tool as a feature extractor for static images, we apply ST modeling techniques
on the features of static images from different time instants extracted by
CNNs. All techniques are trained end-to-end together with a CNN feature
extraction part and evaluated on two publicly available benchmarks: The Jester
and the Something-Something datasets. The Jester dataset contains various
dynamic and static hand gestures, whereas the Something-Something dataset
contains actions of human-object interactions. The common characteristic of
these two benchmarks is that the designed architectures need to capture the
full temporal content of videos in order to correctly classify
actions/gestures. Contrary to expectations, experimental results show that
Recurrent Neural Network (RNN) based ST modeling techniques yield inferior
results compared to other techniques such as fully convolutional architectures.
Codes and pretrained models of this work are publicly available.
