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Violence Detection in Video Using Computer Vision Techniques

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Violence Detection in Video Using Computer Vision Techniques

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E. Bermejo1
, O. Deniz1
, G. Bueno1
, and R. Sukthankar2
1 E.T.S.I.Industriales, Universidad de Castilla-La Mancha
Avda. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain
Oscar.Deniz@uclm.es,
WWW home page: http://visilab.etsii.uclm.es/
2
Intel Labs Pittsburgh and Robotics Institute, Carnegie Mellon, USA
Abstract. Whereas the action recognition community has focused mostly
on detecting simple actions like clapping, walking or jogging, the detection of fights or in general aggressive behaviors has been comparatively
less studied. Such capability may be extremely useful in some video
surveillance scenarios like in prisons, psychiatric or elderly centers or
even in camera phones. After an analysis of previous approaches we test
the well-known Bag-of-Words framework used for action recognition in
the specific problem of fight detection, along with two of the best action
descriptors currently available: STIP and MoSIFT. For the purpose of
evaluation and to foster research on violence detection in video we introduce a new video database containing 1000 sequences divided in two
groups: fights and non-fights. Experiments on this database and another
one with fights from action movies show that fights can be detected with
near 90% accuracy.
Keywords: action recognition, fight detection, video surveillance
1 Introduction
In the last years, the problem of human action recognition at a distance has
become tractable by using computer vision techniques. Although the first approaches obtained good results, they have some limitations too. There are, for
example, aperture problems and discontinuities in optical flow based approaches
[8], and illumination and reinitialization problems in feature tracking approaches
[2]. More recently, the use of feature descriptors around interest points has become popular within the action recognition community, see the recent survey
[16]. This approach analyzes actions by considering the video sequence as a
space-time volume and using gradients, intensities, flows or other local features.
This approach has shown better tolerance to posture, occlusion, illumination
or deformation. On the other hand, current methods usually involve spatiotemporal analysis of 3D descriptors at multiple scales in high resolution videos,
so they require high computational costs.
Approaches based on feature descriptors typically use the well-known bagof-words framework [14, 7]. In this case the output is simply a histogram that
reflects word distribution as frequencies. In order to obtain the histogram, the
bag-of-words representation creates a vocabulary using for example k-means
clustering. The complete procedure is described in Section 4.
The goal of this paper is to assess the performance of modern action recognition approaches for the recognition of fights in videos, movies or video-surveillance
footage. Most of previous work on action recognition focuses on simple human
actions like walking, jumping or hand waving [13]. Despite its potential usefulness, violent action detection has been less studied. Whereas there is a number
of well-studied datasets for action recognition, significant datasets with violent
actions have not been made available. In this work we introduce a fight dataset
and use two of the best action recognition methods currently available (STIP
[12] and MoSIFT [4]) to assess the performance in the fight detection problem.
A violence detector has immediate applicability both in the surveillance domain and for rating/tagging online video content. The primary function of largescale surveillance systems deployed in institutions such as schools, prisons and
elder care facilities is for alerting authorities to potentially dangerous situations.
However, human operators are overwhelmed with the number of camera feeds
and manual response times are slow, resulting in a strong demand for automated alert systems. Similarly, there is increasing demand for automated rating
and tagging systems that can process the great quantities of video uploaded to
websites. The primary contribution of this paper are two-fold. First, we show
that one can construct a versatile and accurate fight detector using a local descriptors approach. Second, we present a new dataset of hockey video containing
fights and demonstrate that our proposed approach can reliably detect violence
in sports footage, even in the presence of camera motion.
The paper is organized as follows. Section 2 analyzes previous work on violence recognition. Next, we describe the new hockey fights dataset in Section 3.
Section 4 presents the two descriptors we use for activity recognition. Then, we
describe the bag-of-words approach and the discriminative classifier. Section 5
details our evaluation methodology and summarizes our experimental results on
the hockey fights dataset. Finally, in Section 6 we summarize key conclusions.
2 Related Work
One of the first proposals for violence recognition in video is Nam et al. [18],
which proposes recognizing violent scenes in videos using flame and blood detection and capturing the degree of motion, as well as the characteristic sounds of
violent events. Cheng et al. [5] recognizes gunshots, explosions and car-braking
in audio using a hierarchical approach based on Gaussian mixture models and
Hidden Markov models (HMM). Giannakopoulos et al. [10] also propose a violence detector based on audio features. Clarin et al. [6] present a system that
uses a Kohonen self-organizing map to detect skin and blood pixels in each frame
and motion intensity analysis to detect violent actions involving blood. Zajdel
et al. [19], introduce the CASSANDRA system, which employs motion features
related to articulation in video and scream-like cues in audio to detect aggression
in surveillance videos.
More recently, Gong et al. [11] propose a violence detector using low-level
visual and auditory features and high-level audio effects identifying potential
violent content in movies. Chen et al. [3] use binary local motion descriptors
(spatio-temporal video cubes) and a bag-of-words approach to detect aggressive behaviors. Lin and Wang [15] describe a weakly-supervised audio violence
classifier combined using co-training with a motion, explosion and blood video
classifier to detect violent scenes in movies. Giannakopoulos et al. [9] present a
method for violence detection in movies based on audio-visual information that
uses a statistics of audio features and average motion and motion orientation
variance features in video combined in a k-Nearest Neighbor classifier to decide
whether the given sequence is violent.
In summary, a number of previous works require audio cues for detecting
violence or rely on color to detect cues such as blood. In this respect, we note
that there are important applications, particularly in surveillance, where audio
is not available and where the video is greyscale. Finally, while explosions, blood
and running may be useful cues for violence in action movies, they are rare in
real-world surveillance video. In this paper, we focus on reliable cues for early
detection of violence in such settings.
3 Dataset
The majority of widely used, publicly-available datasets in action recognition,
such as KTH [13], focus on single actors performing a simple action like walking,
jumping or waving against an uncluttered background; these are clearly unsuitable for evaluating violence detection. Datasets such as INRIA IXMAS, which
show an individual kicking or punching could be used to train (but not evaluate)
fight detection systems. Some datasets like CAVIAR, BEHAVE or CareMedia
contain some instances of people engaged in aggressive behaviors, but that is
not their primary focus.
Our intention is to introduce a new video dataset created specifically for
evaluating violence detection systems, where both normal and violent activities
occur in similar, dynamic settings. To this end, we collected 1000 clips of action
from hockey games of the National Hockey League (NHL), as shown in Fig. 1.
Each clip consists of 50 frames of 720×576 pixels and is manually labeled as
“fight” or “non-fight”. This dataset enables us to easily and robustly measure
the performance of a variety of violence recognition approaches, as shown in
Section 5. Our fight dataset is available by request from the authors.
4 Activity Recognition
Local image features or interest points provide compact and abstract representations of patterns in an image. Analogously, with local spatio-temporal features
it is possible to obtain compact and descriptive representations of motion. In

this respect, two prominent spatio-temporal descriptors are Space-Time Interest
Points (STIP) and Motion SIFT (MoSIFT).
As described in [12], Space-Time Interest Points (STIP) is an extension of
the Harris corner detection operator to space-time. The detected interest points
are characterized by a high variation of the intensity in space, and non-constant
motion in time. These salient points are detected at multiple spatial and temporal scales. Then, HOG (Histograms of Oriented Gradients), HOF (Histograms of
Optical Flow) and a combination of HOG and HOF termed HNF feature vectors
are extracted for 3D video patches in the neighborhood of the detected STIPs.
These features can be used for recognizing motion events with high performance
and they are robust to scale, frequency and velocity variations of the pattern.
MoSIFT [4] is an extension of the popular SIFT [17] image descriptor for
video. The standard SIFT extracts histograms of oriented gradients in the image. The 256-dimensional MoSIFT descriptor consists of two portions: a standard
SIFT image descriptor and an analogous histogram of optical flows, which represents local motion. These descriptors are extracted only from regions of the
image with sufficient motion. The MoSIFT descriptor has shown better performance in recognition accuracy than other state-of-the-art descriptors [4] but the
approach is significantly more computationally expensive than STIP.
On the other hand, the Bag-of-Words (BoW) approach, adopted from the
text retrieval community [14], has recently become popular for image [7] and
video understanding [16]. The approach represents each video sequence as a histogram over a set of visual words to generate a fixed-dimensional encoding that
can be processed using a standard classifier. In a learning phase, the vocabulary of visual words is typically defined as the cluster centers obtained from
k-means clustering over a large collection of sample low-level descriptors (STIP
or MoSIFT descriptors, see above). In our study, we evaluated vocabularies with
50, 100, 150, 200, 300, 500 and 1000 cluster centers.
Given a vocabulary, the next step is to quantize each descriptor extracted
from the given video to the closest visual word, thus generating histograms of
word occurrence. The final step of this BoW approach is the classification of the
histograms. These histograms are high-dimensional vectors that can be classified
using a standard classifier, typically a Support Vector Machine (SVM). It is wellknown that the choice of SVM kernel can significantly affect performance; in our
experiments, we explore the following popular kernels: the histogram intersection
kernel 1, radial basis function (RBF) and Chi-Squared kernel, which is a
variant of RBF that uses χ
2 distance. Kernel parameters are tuned using 5-fold
cross-validation.
5 Experimental Results
The BoW approach using the STIP and MoSIFT descriptors was evaluated on
the 1000-clip hockey fight dataset. In order to assess the impact of vocabulary size, we generated vocabularies of 50, 100, 150, 200, 300, 500 and 1000
words. Table 1 presents the accuracy of fight detection, averaged over 5-fold
cross-validation. For space reasons, we only show here results obtained with the
histogram intersection kernel (HIK) since it consistently outperformed RBF and
χ
2
. We see that the BoW variants all achieve accuracies near 90%, with a slight
improvement with increasing vocabulary size. On this dataset, STIP(HOG) and
MoSIFT perform comparably. The ROC curve for the best of those runs is shown
in Figure 2. Note that this result (MoSIFT on 500-word vocabulary) does not
correspond to the highest result in Table 1 since the latter shows accuracies
averaged over all folds.
Table 1. Accuracy of fight detection on 1000-clip hockey dataset (5-fold CV).
Vocabulary STIP (HOG) + HIK STIP (HOF) + HIK MoSIFT + HIK
50 87.8% 83.5% 87.5%
100 89.1% 84.3% 89.4%
150 89.7% 85.9% 89.5%
200 89.4% 87.5% 90.4%
300 90.8% 87.2% 90.4%
500 91.4% 87.4% 90.5%
1000 91.7% 88.6% 90.9%
Hockey fights contain useful information for learning fight patterns. Still, can
those patterns be translated to other scenarios?. To explore the generalization
capacity of the studied approaches, we also evaluated the fight recognition system
on a second dataset consisting of 200 video clips obtained from action movies (see
Figure 3 for examples), of which 100 contained a fight. Unlike the hockey dataset,
which was relatively uniform both in format and content, these videos depicted
Fig. 2. ROC curve of fight detection on 1000-video Hockey dataset using MoSIFT with
500-word vocabulary and histogram intersection kernel.
a wider variety of scenes and were captured at different resolutions. Table 2
summarizes the results. In this case, STIP (HOF) outperformed STIP (HOG),
but STIP’s overall performance is very poor as compared to MoSIFT (59.0%
vs. 89.5%). Note that increasing vocabulary size does not uniformly improve
recognition accuracy.
We make several observations based on these experiments. First, detecting
fights in televised hockey footage is easier than detecting fights in action movies,
despite the fact that the former contains very similar footage for both classes.
This could partially be attributed to the fact that fights in movies are more varied
in appearance and cinematography while sports footage is relatively consistent.
However, it also indicates that televised hockey fights may exhibit more reliable
cues that a supervised classifier can exploit — for instance, the camera tends
to zoom in to a hockey fight while showing more wide-angle shots during nonfight segments of the hockey game. Second, we see that STIP and MoSIFT are
similar in performance on the former task but MoSIFT is dramatically superior
on the action movie dataset (retaining 90% accuracy levels). This indicates that
the MoSIFT representation, though more computationaly expensive than STIP,
does make a difference.
6 Conclusions
Recognizing fights and aggressive behavior in video is an increasingly important
application area. Such capability may be extremely useful in video surveillance
scenarios like in prisons, psychiatric or elderly centers. Action recognition techniques that have focused largely on individual actors and simple events can be
extended to this specific application. This paper evaluates how state-of-the-art
video descriptors can perform fight detection on two new datasets: a 1000-video
collection of NHL hockey games and a smaller 200-clip collection of scenes from
Fig. 3. Example of a fight sequence from an action movie.
Table 2. Accuracy of fight detection on action movie dataset (5-fold CV).
Vocabulary STIP (HOG) + HIK STIP (HOF) + HIK MoSIFT + HIK
50 44.5% 51.2% 76.0%
100 45.0% 56.5% 79.5%
150 49.0% 59.0% 80.0%
200 46.5% 53.5% 80.0%
300 44.5% 52.5% 87.5%
500 44.5% 50.5% 89.5%
1000 38.5% 52.5% 89.0%
action movies. Experiments show that the popular bag-of-words approach can
accurately recognize fight sequences with approximately 90% accuracy. For the
hockey dataset, we observed that accuracy was insensitive to the choice of lowlevel feature descriptor and vocabulary size; however, on the second dataset, the
choice of descriptor was critical, with MoSIFT dramatically outperforming the
best STIP under all conditions. The promising performance of action recognition methods on this challenging task shows that a versatile marketable fight
detector may be feasible.
7 Acknowledgements
This work was partially funded by Project PII2I09-0043-3364 of Castilla-La Mancha Regional Government. We thank Drs. Alex Hauptmann and Ming-yu Chen
for providing the MoSIFT descriptor and their helpful suggestions.

Reference Docs:- https://www.cs.cmu.edu/~rahuls/pub/caip2011-rahuls.pdf

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