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.

本研究提出了一种高效的双流深度学习架构，利用可分离卷积 LSTM 和预训练的 MobileNet，其中一条流接收抑制背景的帧作为输入，另一条流处理相邻帧的差异，通过简单且快速的输入预处理技术突出帧中的运动物体，从而产生有区分度的特征，实验结果表明该模型在计算效率和检测精度方面都有着卓越表现，特别是在 RWF-2000 数据集上，优于同类模型。