  This paper provides an insight into the possibility of scene recognition from
a video sequence with a small set of repeated shooting locations (such as in
television series) using artificial neural networks. The basic idea of the
presented approach is to select a set of frames from each scene, transform them
by a pre-trained singleimage pre-processing convolutional network, and classify
the scene location with subsequent layers of the neural network. The considered
networks have been tested and compared on a dataset obtained from The Big Bang
Theory television series. We have investigated different neural network layers
to combine individual frames, particularly AveragePooling, MaxPooling, Product,
Flatten, LSTM, and Bidirectional LSTM layers. We have observed that only some
of the approaches are suitable for the task at hand.
