Learning to Detect Violent Videos using Convolutional Long Short-Term
Memory

arXiv:1709.06531v1 [] 19 Sep 2017

Swathikiran Sudhakaran1,2 and Oswald Lanz2
1

2

University of Trento, Trento, Italy
Fondazione Bruno Kessler, Trento, Italy
{sudhakaran,lanz}@fbk.eu

Abstract
Developing a technique for the automatic analysis of
surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a
deep neural network for the purpose of recognizing violent
videos. A convolutional neural network is used to extract
frame level features from a video. The frame level features
are then aggregated using a variant of the long short term
memory that uses convolutional gates. The convolutional
neural network along with the convolutional long short term
memory is capable of capturing localized spatio-temporal
features which enables the analysis of local motion taking
place in the video. We also propose to use adjacent frame
differences as the input to the model thereby forcing it to encode the changes occurring in the video. The performance
of the proposed feature extraction pipeline is evaluated on
three standard benchmark datasets in terms of recognition
accuracy. Comparison of the results obtained with the state
of the art techniques revealed the promising capability of
the proposed method in recognizing violent videos.

1. Introduction
Nowadays, the amount of public violence has increased
dramatically. This can be a terror attack involving one or
a number of persons wielding guns to a knife attack by a
single person. This has resulted in the ubiquitous usage of
surveillance cameras. This has helped authorities in identifying violent attacks and take the necessary steps in order to
minimize the disastrous effects. But almost all the systems
nowadays require manual human inspection of these videos
for identifying such scenarios, which is practically infeasible and inefficient. It is in this context that the proposed
study becomes relevant. Having such a practical system that
can automatically monitor surveillance videos and identify

the violent behavior of humans will be of immense help and
assistance to the law and order establishment. In this work,
we will be considering aggressive human behavior as violence rather than the presence of blood or fire.
The development of several deep learning techniques,
brought about by the availability of large datasets and computational resources, has resulted in a landmark change
in the computer vision community. Several techniques
with improved performance for addressing problems such
as object detection, recognition, tracking, action recognition, caption generation, etc. have been developed as a
result. However, despite the recent developments in deep
learning, very few deep learning based techniques have
been proposed to tackle the problem of violence detection
from videos. Almost all the existing techniques rely on
hand-crafted features for generating visual representations
of videos. The most important advantage of deep learning
techniques compared to the traditional hand-crafted feature
based techniques is the ability of the former to achieve a
high degree of generalization. Thus they are able to handle unseen data in a more effective way compared to handcrafted features. Moreover, no prior information about the
data is required in the case of a deep neural network and
they can be inputted with raw pixel values without much
complex pre-processing. Also, deep learning techniques
are not application specific unlike the hand-crafted feature
based methods since a deep neural network model can be
easily applied for a different task without any significant
changes to the architecture. Owing to these reasons, we
choose to develop a deep neural network for performing violent video recognition.
Our contributions can be summarized as follows:
• We develop an end-to-end trainable deep neural network model for performing violent video classification
• We show that a recurrent neural network capable of encoding localized spatio-temporal changes generates a

better representation, with less number of parameters,
for detecting the presence of violence in a video
• We show that a deep neural network trained on the
frame difference performs better than a model trained
on raw frames
• We experimentally validate the effectiveness of the
proposed method using three widely used benchmarks
for violent video classification
The rest of the document is organized as follows. Section
2 discusses some of the relevant techniques for performing
violent video recognition followed by a detailed explanation
of the proposed deep neural network model in Section 3.
The details about the various experiments conducted as part
of this research are given in Section 4 and the document is
concluded in Section 5.

2. Related Works
Several techniques have been proposed by researchers
for addressing the problem of violence detection from
videos. These include methods that use the visual content [21, 2], audio content [23, 12] or both [33, 1]. In this
section, we will be concentrating on methods that use the
visual cues alone since it is more related to the proposed
approach and moreover audio data is generally unavailable
with surveillance videos. All the existing techniques can be
divided into two classes depending on the underlying idea
1. Inter-frame changes: Frames containing violence undergo massive variations because of fast motion due to
fights [28, 5, 4, 8]
2. Local motion in videos: The motion change patterns taking place in the video is analyzed [6, 3, 7, 21, 15, 32, 20, 24,
13, 2, 11, 34]
Vasconcelos and Lippman [28] used the tangent distance
between adjacent frames for detecting the inter-frame variations. Clarin et al. improves this method in [5] by finding the regions with skin and blood and analyzing these regions for fast motion. Chen et al. [4] uses the motion vector
encoded in the MPEG-1 video stream for detecting frames
with high motion content and then detects the presence of
blood for classifying the video as violent. Deniz et al. [8]
proposes to use the acceleration estimate computed from
the power spectrum of adjacent frames as an indicator of
fast motion between successive frames.
Motion trajectory information and the orientation of
limbs of the persons present in the scene is proposed as a
measure for detecting violence by Datta et al. [6]. Several
other methods follow the techniques used in action recognition, i.e., to identify spatio-temporal interest points and extract features from these points. These include Harris corner
detector [3], Space-time interest points (STIP) [7], motion

scale-invariant feature transform (MoSIFT) [21, 32]. Hassner et al. [15] introduces a new feature descriptor called
violent flows (ViF), which is the flow magnitude over time
of the optical flow between adjacent frames, for detecting
violent videos. This method is improved by Gao et al. [11]
by incorporating the orientation of the violent flow features
resulting in oriented violent flows (OViF) features. Substantial derivative, a concept in fluid dynamics, is proposed by
Mohammadi et al. [20] as a discriminative feature for detecting violent videos. Gracia et al. [13] proposes to use the
blob features, obtained by subtracting adjacent frames, as
the feature descriptor. The improved dense trajectory features commonly used in action recognition is used as a feature vector by Bilinski et al. in [2]. They also propose an
improved Fisher encoding technique that can encode spatiotemporal position of features in a video. Zhang et al. [34]
proposes to use a modified version of motion Weber local
descriptor (MoIWLD) followed by sparse representation as
the feature descriptor.
The hand-crafted feature based techniques used methods
such as bag of words, histogram, improved Fisher encoding, etc. for aggregating the features across the frames.
Recently various models using long short term memory
(LSTM) RNNs [16] have been developed for addressing
problems involving sequences such as machine translation
[27], speech recognition [14], caption generation [31, 29]
and video action recognition [9, 26]. The LSTM was introduced in 1997 to combat the effect of vanishing gradient
problem which was plaguing the deep learning community.
The LSTM incorporates a memory unit which contains information about the inputs the LSTM unit has seen and is
regulated using a number of fully-connected gates. The
same idea of using LSTM for feature aggregation is proposed by Dong et al. in [10] for violence detection. The
method consisted of extracting features using a convolutional neural network from raw pixels, optical flow images
and acceleration flow maps followed by LSTM based encoding and a late fusion.
Recently, Xingjian et al. [30] replaced the fullyconnected gate layers of the LSTM with convolutional layers and used this improved model for predicting precipitation nowcasting from radar images with improved performance. This newer model of the LSTM is named as
convolutional LSTM (convLSTM). Later, it has been used
for predicting optical flow images from videos [22] and
for anomaly detection in videos [18]. By replacing the
fully-connected layers in the LSTM with convolutional layers, the convLSTM model is capable of encoding spatiotemporal information in its memory cell.

3. Proposed method
The goal of the proposed study was to develop an endto-end trainable deep neural network model for classifying

Frame 1
ConvLSTM

Frame 2

Frame 2

96

256

384

384

256

96

256

384

384

256

96

256

384

384

256

Hidden
State (256)

Frame 3

Frame N-1

Frame N
10

2

256
1000

Figure 1. Block diagram of the proposed model. The model consists of alternating convolutional (red), normalization (grey) and pooling
(blue) layers. The hidden state of the ConvLSTM (green) at the final time step is used for classification. The fully-connected layers are
shown in brown colour.

videos in to violent and non-violent ones. The block diagram of the proposed model is illustrated in figure 1. The
network consists of a series of convolutional layers followed
by max pooling operations for extracting discriminant features and convolutional long short memory (convLSTM) for
encoding the frame level changes, that characterizes violent
scenes, existing in the video.

3.1. ConvLSTM
Videos are sequences of images. For a system to identify if a fight is taking place between the humans present
in the video, it should be capable of identifying the locations of the humans and understand how the motion of the
said humans are changing with time. Convolutional neural networks (CNN) are capable of generating a good representation of each video frame. For encoding the temporal changes a recurrent neural network (RNN) is required.
Since we are interested in changes in both the spatial and
temporal dimensions, convLSTM will be a suitable option.
Compared to LSTM, the convLSTM will be able to encode
the spatial and temporal changes using the convolutional
gates present in them. This will result in generating a better
representation of the video under analysis. The equations of
the convLSTM model are given in equations 1-6.
it = σ(wxi ∗ It + whi ∗ ht−1 + bi )

(1)

ft = σ(wxf ∗ It + whf ∗ ht−1 + bf )
c̃t = tanh(wxc̃ ∗ It + whc̃ ∗ ht−1 + bc̃ )

(2)
(3)

ct = c̃t ⊙ it + ct−1 ⊙ ft
ot = σ(wxo ∗ It + who ∗ ht−1 + bo )

(4)
(5)

ht = ot ⊙ tanh(ct )

(6)

In the above equations, ‘*’ represents convolution operation and ‘⊙’ represents the Hadamard product. The hidden
state ht , the memory cell ct and the gate activations it , ft
and ot are all 3D tensors in the case of convLSTM.

For a system to identify a video as violent or non-violent,
it should be capable of encoding localized spatial features
and the manner in which they change with time. Handcrafted features are capable of achieving this with the downside of having increased computational complexity. CNNs
are capable of generating discriminant spatial features but
existing methods use the features extracted from the fullyconnected layers for temporal encoding using LSTM. The
output of the fully-connected layers represents a global descriptor of the whole image. Thus the existing methods fail
to encode the localized spatial changes. As a result, they resort to methods involving addition of more streams of data
such as optical flow images [10] which results in increased
computational complexity. It is in this context that the use
of convLSTM becomes relevant as it is capable of encoding the convolutional features of the CNN. Also, the convolutional gates present in the convLSTM is trained to encode the temporal changes of local regions. In this way,
the whole network is capable of encoding localized spatiotemporal features.

3.2. Network Architecture
Figure 1 illustrates the architecture of the network used
for identifying violent videos. The convolutional layers
are trained to extract hierarchical features from the video
frames and are then aggregated using the convLSTM layer.
The network functions as follows: The frames of the video
under consideration are applied sequentially to the model.
Once all the frames are applied, the hidden state of the
convLSTM layer in this final time step contains the representation of the input video frames applied. This video representation, in the hidden state of the convLSTM, is then
applied to a series of fully-connected layers for classification.
In the proposed model, we used the AlexNet model [17]
pre-trained on the ImageNet database as the CNN model for
extracting frame level features. Several studies have found

Table 1. Classification accuracy obtained with the hockey fight
dataset for different models

Input
Video Frames
(random initialization)
Video Frames
(ImageNet pre-trained)
Difference of Video Frames
(random initialization)
Difference of Video Frames
(ImageNet pre-trained)

Classification Accuracy
94.1±2.9%
96±0.35%
95.5±0.5%
97.1±0.55%

out that networks trained on the ImageNet database is capable of having better generalization and results in improved
performance for tasks such as action recognition [25] [19].
In the convLSTM, we used 256 filters in all the gates with a
filter size of 3 × 3 and stride 1. Thus the hidden state of the
convLSTM consists of 256 feature maps. A batch normalization layer is added before the first fully-connected layer.
Rectified linear unit (ReLU) non-linear activation is applied
after each of the convolutional and fully-connected layers.
In the network, instead of applying the input frames as
such, the difference between adjacent frames are given as
input. In this way, the network is forced to model the
changes taking place in adjacent frames rather than the
frames itself. This is inspired by the technique proposed
by Simonyan and Zisserman in [25] to use optical flow images as input to a neural network for action recognition. The
difference image can be considered as a crude and approximate version of optical flow images. So in the proposed
method, the difference between adjacent video frames are
applied as input to the network. As a result, the computational complexity involved in the optical flow image generation is avoided. The network is trained to minimize the
binary cross entropy loss.

rithm. Since the number of videos present in the datasets
are limited, data augmentation techniques such as random
cropping and horizontal flipping are used during training
stage. During each training iteration, a portion of the frame
of size 224 × 224 is cropped, from the four corners or from
the center, and is randomly flipped before applying to the
network. Note that the same augmentation technique is followed for all the frames present in a video. The network is
run for 7500 iterations during the training stage. In the evaluation stage, the video frames are resized to 224 × 224 and
are applied to the network for classifying them as violent or
non-violent. All the training video frames in a dataset are
normalized to make their mean zero and variance unity.

4.2. Datasets

To evaluate the effectiveness of the proposed approach
in classifying violent videos, three benchmark datasets are
used and the classification accuracy is reported.

The performance of the proposed method is evaluated on three standard public datasets namely, Hockey
Fight Dataset [21], Movies Dataset [21] and Violent-Flows
Crowd Violence Dataset [15]. They contain videos captured
using mobile phones, CCTV cameras and high resolution
video cameras.
Hockey Fight Dataset: Hockey fight dataset is created by
collecting videos of ice hockey matches and contains 500
fighting and non-fighting videos. Almost all the videos
in the dataset have a similar background and subjects (humans). 20 frames from each video are used as inputs to the
network.
Movies Dataset: This dataset consists of fight sequences
collected from movies. The non-fight sequences are collected from other publicly available action recognition
datasets. The dataset is made up of 100 fight and 100 nonfight videos. As opposed to the hockey fight dataset, the
videos of the movies dataset is substantially different in its
content. 10 frames from each video are used as inputs to the
network.
Violent-Flows Dataset: This is a crowd violence dataset as
the number of people taking part in the violent events are
very large. Most of the videos present in this dataset are
collected from violent events taking place during football
matches. There are 246 videos in this dataset. 20 frames
from each video are used as inputs to the network.

4.1. Experimental Settings

4.3. Results and Discussions

The network is implemented using the Torch library.
From each video, N number of frames equally spaced in
time are extracted and resized to a dimension of 256 × 256
for training. This is to avoid the redundant computations
involved in processing all the frames, since adjacent frames
contain overlapping information. The number of frames selected is based on the average duration of the videos present
in each dataset. The network is trained using RMSprop algorithm with a learning rate of 10−4 and a batch size of
16. The model weights are initialized using Xavier algo-

Performance evaluation is done using 5-folds cross validation scheme, which is the technique followed in existing literature. The model architecture selection was done
by evaluating the performance of the different models on
the hockey fight dataset. The classification accuracies obtained for the two cases, video frames as input and difference of frames as input, is listed in table 1. From the table,
it can also be seen that using a network that is pre-trained on
the ImageNet dataset (we used BVLC AlexNet from Caffe
model zoo) results in better performance compared to us-

4. Experiments and Results

Table 2. Comparison of classification results

Method
MoSIFT+HIK[21]
ViF[15]
MoSIFT+KDE+Sparse Coding[32]
Deniz et al.[8]
Gracia et al.[13]
Substantial Derivative[20]
Bilinski et al.[2]
MoIWLD[34]
ViF+OViF[11]
Three streams + LSTM[10]
Proposed

Hockey Dataset
90.9%
82.9±0.14%
94.3±1.68%
90.1±0%
82.4±0.4%
93.4
96.8±1.04%
87.5±1.7%
93.9
97.1±0.55%

ing a network that is randomly initialized. In this way, we
decided to use frame difference as the input and to use a
pre-trained network in the model. Table 2 gives the classification accuracy values obtained for the various datasets
considered in this study and is compared against 10 state of
the art techniques. From the table, it can be seen that the
proposed method is able to better the results of the existing
techniques in the case of hockey fights dataset and movies
dataset.
As mentioned earlier, this study considers aggressive behavior as violent. The biggest problem of considering this
definition occurs in the case of sports. For instance, in the
hockey dataset, the fight videos consists of players colliding against each other and hitting one another. So one easy
way to detect violent scenes is to check if one player moves
closer to another. But the non-violent videos also consist
of players hugging each other or doing high fives as part
of a celebration. It is highly likely that these videos could
be mistaken as violent. But the proposed method is able
to avoid this which suggests that it is capable of encoding
motion of localized regions (motion of limbs, reaction of involved persons, etc.). However, in the case of violent-flows
dataset, the proposed method is not able to best the previous
state of the art technique (it came second in terms of accuracy). Analyzing the dataset, it is found that in most of the
violent videos, only a small part of the crowd is found to be
involved in aggressive behavior while a large part remained
as spectators. This forces the network to mark such videos
as non-violent since majority of the people present in it is
found to behave normally. Further studies are required for
devising techniques to alleviate this problem involved with
crowd videos. One technique that can be considered is to
divide the frame in to sub-regions and predict the output of
the regions separately and mark the video as violent if any
of the regions is outputted by the network as violent.
In order to compare the advantage of convLSTM over
traditional LSTM, a different model that consists of LSTM
is trained and tested on the hockey fights dataset. The new

Movies Dataset
89.5%
98.0±0.22%
97.8±0.4%
96.89±0.21%
99
100±0%

Violent-Flows Dataset
81.3±0.21%
89.05±3.26%
85.43±0.21%
96.4
93.19±0.12%
88±2.45%
94.57±2.34%

Table 3. Comparison between convLSTM and LSTM models in
terms of classification accuracy obtained in the hockey fights
dataset and number of parameters

Model
convLSTM
LSTM

Accuracy
97.1±0.55%
94.6±1.19%

No. of Parameters
9.6M(9619544)
77.5M(77520072)

model consists of the AlexNet architecture followed by an
LSTM RNN layer. The output of the last fully-connected
layer (fc7) of AlexNet is applied as input to an LSTM with
1000 units. The rest of the architecture is similar to the one
that uses convLSTM. The results obtained with this model
and the number of trainable parameters associated with it
are compared against the proposed model in table 3. The table clearly shows the advantages of using convLSTM over
LSTM and the capability of convLSTM in generating useful video representation. It is also worth mentioning that
the number of parameters that are required to be optimized,
in the case of convLSTM, is very much less compared to
LSTM (9.6M vs 77.5M). This helps the network to generalize better without overfitting in the case of limited data.
The proposed model is capable of processing 31 frames per
second on an NVIDIA K40 GPU.

5. Conclusions
This work presents a novel end-to-end trainable deep
neural network model for addressing the problem of violence detection in videos. The proposed model consists of
a convolutional neural network (CNN) for frame level feature extraction followed by feature aggregation in the temporal domain using convolutional long short term memory
(convLSTM). The proposed method is evaluated on three
different datasets and resulted in improved performance
compared to the state of the art methods. It is also shown
that a network trained to model changes in frames (frame
difference) performs better than a network trained using
frames as inputs. A comparative study between the tradi-

tional fully-connected LSTM and convLSTM is also done
and the results show that the convLSTM model is capable of
generating a better video representation compared to LSTM
with less number of parameters, thereby avoiding overfitting.

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