  Automatically detecting violence from surveillance footage is a subset of
activity recognition that deserves special attention because of its wide
applicability in unmanned security monitoring systems, internet video
filtration, etc. In this work, we propose an efficient two-stream deep learning
architecture leveraging Separable Convolutional LSTM (SepConvLSTM) and
pre-trained MobileNet where one stream takes in background suppressed frames as
inputs and other stream processes difference of adjacent frames. We employed
simple and fast input pre-processing techniques that highlight the moving
objects in the frames by suppressing non-moving backgrounds and capture the
motion in-between frames. As violent actions are mostly characterized by body
movements these inputs help produce discriminative features. SepConvLSTM is
constructed by replacing convolution operation at each gate of ConvLSTM with a
depthwise separable convolution that enables producing robust long-range
Spatio-temporal features while using substantially fewer parameters. We
experimented with three fusion methods to combine the output feature maps of
the two streams. Evaluation of the proposed methods was done on three standard
public datasets. Our model outperforms the accuracy on the larger and more
challenging RWF-2000 dataset by more than a 2% margin while matching
state-of-the-art results on the smaller datasets. Our experiments lead us to
conclude, the proposed models are superior in terms of both computational
efficiency and detection accuracy.
