=Paper=
{{Paper
|id=Vol-3097/paper29
|storemode=property
|title=A Proposal on Stampede Detection in Real Environments
|pdfUrl=https://ceur-ws.org/Vol-3097/paper29.pdf
|volume=Vol-3097
|authors=Antonio Carlos Cob-Parro,Cristina Losada-Gutiérrez,Marta Marrón-Romera,Alfredo Gardel-Vicente,Ignacio Bravo-Muñoz ,Mohammad Ibrahim Sarker
|dblpUrl=https://dblp.org/rec/conf/ipin/Cob-ParroLRVMS21
}}
==A Proposal on Stampede Detection in Real Environments==
https://ceur-ws.org/Vol-3097/paper29.pdf
A Proposal on Stampede Detection in Real
Environments
Antonio Carlos Cob-Parro, Cristina Losada-Gutiérrez, Marta Marrón-Romera,
Alfredo Gardel-Vicente, Ignacio Bravo-Muñoz and Mohammad Ibrahim Sarker
Abstract
It is a fact that the world population has grown in recent decades, as well as the number of social and
tourism events, generating situations of agglomerations where different problems may lead to generate
bottlenecks stampedes or falls, that can be a risk for people. Thus, the study of the behaviour of crowds
is a relevant research topic. In this context, this paper presents and approach for real-time stampede
detection from images, in low and medium crowd scenarios. The proposal is based on a feature vector
extracted from the optical flow entropy, and this does not require the use of thresholds. Instead of that,
it includes a a Stacking classifier, based on the union of a random forest with ten estimators and an
support vector classifier, that works properly in the different analyzed scenarios. The proposal has been
evaluated in UMN and PETS 2009 datasets and compared to other state-of-the-art proposals in terms
of accuracy and computational cost. However, since the provided ground-truth was not accurate, a
new manually-labelled ground-truth has been generated and make publicly available to the scientific
community. The obtained results allows validating the proposal, outperforming the state-of-the-art
methods both in terms of accuracy and computational cost in all the evaluated scenarios.
1. Introduction
It is a fact that the world population has grown in recent decades. In 1950s, there was a
population of 2.5 billion people, while in the year 2020, there are approximately 7.7 billion
people. This fact is more shocking when the increase in the last ten years is approximately 1
billion people, suggesting that the population is increasing in a non-linear way year by year.
Some population experts suggest that for the next century, the population could exceed 11 billion
people. This situation creates a scenario in which it is becoming relevant to deploy surveillance
systems capable of detecting individual behaviour and group behaviour. The number of social
and tourism events will grow, generating situations of agglomerations where different problems
may lead to generate bottlenecks stampedes, falls and a long plethora of risk scenarios. The
study of crowds’ behaviour is a relevant research topic, to be able to control and protect people
in the face of uncontrolled events.
IPIN 2021 WiP Proceedings, November 29 – December 2, 2021, Lloret de Mar, Spain
Envelope-Open antonio.cob@edu.uah.es (A. C. Cob-Parro); cristina.losada@uah.es (C. Losada-Gutiérrez); marta.marron@uah.es
(M. Marrón-Romera); alfredo.gardel@uah.es (A. Gardel-Vicente); ignacio.bravo@uah.es (I. Bravo-Muñoz);
ibrahim.sarker@uah.es (M. I. Sarker)
Orcid 0000-0001-8608-7351 (A. C. Cob-Parro); 0000-0001-9545-327X (C. Losada-Gutiérrez); 0000-0002-9421-8566
(M. Marrón-Romera); 0000-0001-7887-4689 (A. Gardel-Vicente); 0000-0002-6964-0036 (I. Bravo-Muñoz);
0000-0002-9589-294X (M. I. Sarker)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Crowd analysis, in general, can be approached holistically or by object-based methods. object
based methods analyse crowds as sets of objects, studying people in a particular way [1],
being these objects detected and followed in a particular way. Then in this type of systems,
the amount and kind of tracking performed are analysed. The problem of these object-based
methods is the accuracy because, in dense crowds, the identification of people is complicated.
On the other hand, holistic approaches are based on identifying the crowd as a single unit [2, 3].
These methods are based on extracting the characteristics of the crowd to deduce its behaviour.
Holistic methods have a reasonable accuracy rate in detecting anomalous behaviour.
In this work, we have focused on the detection of anomalous behavior in crowds. In particular,
in the behaviour related to stampedes. Detection systems usually have two differentiated phases.
The first phase is based on the representation of the event by a set of characteristics. There
are many methods for the extraction of features, such as the study of social force [2], which
is based on the measurement of the internal motivations of individuals to perform specific
actions. The use of histograms of optical flow [4] to describe movement patrons or the use of
histograms of movement direction [5] to describe direction patrons. The second phase consists
of momentum detection from the previously extracted features. These classification models are
usually characterized by having only two classes, either a stampede or not. There are different
methods such as Support Vector Machine [6], neural replicator network [7], convolutional
networks [8], etc.
The latest research in the detection of abnormal behaviour in crowds uses technologies such
as context location and motion-rich patio-temporal volumes [9],temporal convolutional neural
network pattern [10], generative adversarial networks [11], global event influence model [12].
In this work, we have implemented a system that draws from both the most modern and the
most classical approaches, Being a system based on the use of the value of the magnitude of the
optical flow and from there extract the entropy to generate a series of descriptors that are used
in a machine learning model for the detection of the anomaly.
Considering the previous research work, the main contribution of this paper is to deploy a
robust and reliable system capable to detect stampedes in real-time. In addition, the algorithm
to detect events’ peak does not require a threshold. Additionally, we have manually labelled the
UMN [2] and PETS 2009 [13] datasets to quantitatively evaluate the stampedes detection, and
the result of the annotation has been made available to the scientific community [14].
The rest of the paper is organized as follows: section 2 describes the proposal for stampede
detection, them section 3 presents the annotation procedure for the UMN and PETS-2009
datasets. Next, the main experimental results are shown in section 4.1. Finally, section 5
describes the main conclusions and future work.
2. Proposal for stampede detection
We have focused our efforts on identifying stampedes in the first two types of scenarios, the
low and intermediate density crowds. As other previous approaches, such as the one described
in [15], the proposal presented in this work is based on analysing the entropy obtained from the
scene optical flow. It is obtained a dense optical flow by using the method of Farneback [16],
instead of a punctual one as in the case of using the method of Lucas-Kanade [17].
Besides, the system works in real time. Moreover, it does not require a threshold (that must
be modified for each dataset). Instead, we have extracted a feature vector from the entropy, and
designed a machine learning model based on stacking classifier for stampede detection that
uses a set of features generated from the entropy signal extracted from the optical flow.
The figure 1 shows a general block diagram with each of the stages of the system. Below,
there are described in detail each of these stages.
Figure 1: General block diagram of the system.
2.1. Optical flow computation
The system extracts the entropy using the optical flow of the image. For this purpose, we have
used the dense optical flow of Farneback instead of the point optical flow of Lucas-Kanade.
Because we wanted a system that would analyse the whole image.This system obtains the
movement variations of all pixels between frames. Unlike Lucas-Kanade, which is based on the
study of the variations of a specific set of pixels.
The Farneback’s method is based on the estimation of the movement of the pixels between
the actual and previous frames. From that movement, there are generated the displacement
vectors, that are then used to study the movement variations in the image. This type of optical
flow analysis presents a higher accuracy than those algorithms based on sparse optical flow.
Thus, Farneback’s dense optical flow extraction is based on expanding the position of the pixel
coordinates by polynomial expansion using the neighbourhood information of each pixel in
the image. The original coordinates (𝑢0 , 𝑣0 ) are independent variables, and the new coordinates
(𝑢, 𝑣) are polynomials of dependent variables. The amount of motion (𝑑𝑢, 𝑑𝑣) of the pixel in
𝑢 and 𝑣 directions are determined by substituting the coordinates into them. A displacement
vector is obtained for each pixel between two frames.
2.2. Feature extraction
The feature vector used for detecting stampedes is based on the entropy. To obtain the entropy,
first, the image is pre-processed by blurring and grayscaling. Then the magnitude value of the
optical flow is extracted using the current and previous frames These magnitude values form
a matrix that correspond to the movement variation of each pixel. Then, these matrices are
grouped in batches of 20 frames, and the mean of all the magnitudes is extracted for each pixel.
Then the median is calculated, and the result is called activity map. From the activity map, the
entropy for that frame can be extracted. To extract the entropy it is used equation 1, where 𝑥 is
the number of separate symbols, 𝑝𝑖 is the frequency of the each pixel in the image and 𝑛 is the
actual frame.
𝑥
𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑛) = ∑ 𝑝𝑖 𝑙𝑜𝑔2 (𝑝𝑖 ) (1)
𝑖=1
In previous works, such as [15], the authors use two thresholds to determine if there was a
stampede or not. The first one was based on the entropy value, and the second one was based
on the temporal occupancy variation (TOV) value between frames. For this work, the TOV has
not been employed using only the entropy value, from what there is obtained a feature vector
for the stacking classifier.
After obtaining the entropy, to determine if there is a stampede or not, we extract a set of
features that are next classified using a stacking classifier. These features include the mean and
standard deviation of the entropy. The mean is computed using a sliding window of 20 frames
as shown in the equation 2, being 𝑘 the size of the sliding windows . Thus the proposal requires
a minimum of 20 frames for detecting a stampede, but it smooths the signal and removes the
high frequency noise.
𝑛
1
𝜇(𝑛) = ∑ 𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑖) (2)
𝑘 𝑖=𝑛−𝑘+1
The standard deviation of the entropy descriptor is used to detect the fast changes in the
signal. A considerable variation generates a significant change of value in this descriptor. The
equation 3 shows the mathematical definition used for the extraction of the descriptor, where 𝑥𝑖
are the current entropy values and 𝜇 the mean entropy value.
𝑛
∑𝑖=𝑛−𝑘+1 (𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑖) − 𝜇(𝑖))2
𝜎 (𝑛) = (3)
√ 𝑘
The third feature is the distance generated as the difference between the mean plus standard
deviation and the mean minus standard deviation. The standard deviation is multiplied because
this value (𝐿) is smaller than the other signal. In the equation 4 is defined mathematically.
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑛) = (𝜇(𝑛) + 𝐿 ∗ 𝜎 (𝑛)) − (𝜇(𝑛) − 𝐿 ∗ 𝜎 (𝑛)) (4)
An example of the signals explained below are shown in the figure 2. The peak of the entropy
(right signal) reflects the moment when the stampede happens. The features explained below
are drawn in the left picture. In blue, it is shown the entropy mean in yellow, the standard
deviation and in green with shade the distance.
Figure 2: The graph on the right shows the entropy value, with the peak at the time of the stampede.
The left plot shows the mean value in blue, the standard deviation in yellow and the distance generated
in green with shading.
2.3. Classification
The stacking classifier model (figure 3) has been used, which is a combination of an support
vector classifier (SVC), random forest (RF). It is an ensemble learning technique for combining
multiple classification models through a meta-classifier. The individual classification models
are trained based on the complete training set; then, the meta-classifier is tuned based on the
results-meta-features of the individual classification models in the ensemble. The meta-classifier
can be trained on the predicted class labels or the ensemble probabilities. Figure 3 shows the
basic structure of a stacking classifier.
Figure 3: Example of stacking classifier structure
The stacking classifier used in this work is based on the union of a random forest with ten
estimators and an SVC, that has been used to classify if there is a stampede or not. The training
was performed with half of the UMN videos and half of the PETS videos, being the other half
used for model testing. This 50:50 margin is used for two reasons: first, a stacking classifier does
not need a large amount of information to train and second, the number of cases in which there
is a stampede in the videos is much smaller than in those in which there is not, so more videos
have been used for testing to achieve a more reliable result. In order to obtain the best results
in terms of measuring the predictive quality of the models, a k-fold of the training process has
been performed with a value of k equal to 10. The value of 𝑘 is 10, because the datasets have
a small number of frames and a small number of labels, so the division into 10 slots provides
enough information in each slot for the training to succeed.
3. Stampede Annotation
To evaluate the system, we have used two datasets, UMN and PETS2009, that have been
widely used in other works for stampede detection. These datasets include several videos with
stampedes. The UMN dataset include the ground-truth, however, when analysing the videos,
it can be seen that there is a delay between the beginning of the stampede and the frame in
which it is labelled. Furthermore, PETS dataset does not include ground-truth information for
stampede events.
The characteristics of the datasets are shown in the table 1. Both datasets have similar types
of stampedes, in which people run either in the same direction or spread out. The way of
recording the videos is the same for both datasets by high-angle shot. The lighting is constant
in all videos but one environment of the UMN dataset. The big difference between the two
datasets is the number of people in UMN is not more than 20 people in any video, while in
PETS there are more than 30 people per video.
Table 1
Main characteristics of the analysed datasets
Dataset Scenario Resolution Illumination #people #videos/frames
Lawn 15 2/1433
Constant
UMN Plaza 240 × 320 12 3/4038
Indoor Variable 10 6/2031
PETS Street-1 41 4/1812
576 × 768 Constant
2009 Street-2 42 4/1060
Due to the lack of an accurate ground-truth, we have analysed the two dataset, and hand-
labelled the information. To label the videos, it is necessary to define two moments in a stampede,
the beginning and the end of the stampede. To label the initial moment we have considered
that more than four people in the image are already prepared to run or moving. The end of the
stampede is defined when the people in the image stop running and start walking or when less
than three people are running on the screen. By means of these guidelines, we have defined
a ground-truth made by hand analysing frame by frame and indicating the moment when
each stampede starts and ends. Figure 4 shows a ground-truth scheme indicating in green
the moments of calm and in red the times where it is considered that there is a stampede. In
addition, in Figure 4, the frames where the stampede begins and ends and the total number of
frames of each video are shown.
Figure 4: Ground-truth UMN
Figure 5 shows a scheme of those PETS videos that contain stampedes. Note that the PETS
videos have the same video but seen from different camera points. For this reason, the beginnings
and endings of the stampede are the same for all these recordings.
As mentioned in previous sections, this more accurate annotation has been made publicly
available [14].
4. Experimental results
4.1. Experimental Set-up
To evaluate the system performance, we have analysed two key parameters. The first one is the
speed of execution of the system, and the second one the accuracy to detect stampedes.
Figure 5: Ground-truth PETS
The computational cost has been evaluated for the original system executed on a CPU (Intel
Core i7-9700K CPU @ 3.60GHz x 8). It is important to note that the OpenCV version is 4.5.1,
and the programming language is Python version 3.7.
Regarding the accuracy, as it has been stated before, two datasets (UMN and PETS) have been
used, which have been compared with the results obtained in the work [15] . The UMN dataset
includes a total of 7502 frames with a resolution of 240x320 at 30 fps. The dataset has been
divided into three scenarios, two outdoor and one indoor. The PETS dataset includes a single
outdoor scenario with a total of 2872 frames at a resolution of 768x576 at 30 fps.
To evaluate the system we have decided to use the receiver operating characteristic (ROC)
and area under the curve (AUC) metrics. A ROC curve is a graph showing the performance of a
classification model at all classification thresholds. This curve represents the true positives rate
(TPR) versus false positives rate (FPR) at different classification thresholds, whereas the AUC
measures the entire two-dimensional area below the total ROC curve. The AUC provides an
aggregate measure of performance for all possible classification thresholds.
4.2. Stampede detection
This section shows the results of stampede detection in different environments, both indoors
and outdoors corresponding to the previously described UMN and PETS 2009 datasets.
As explained previously, entropy, mean, standard deviation and distance combination among
entropy, mean and standard deviation are extracted of each frame. These values are analysed
by a stacking classifier that determine the value of the activity.
Figure 6 shows an example of UMN where it can be clearly observed where the stampede
starts, marked with an arrow.
Figure 6: Green shading indicates the frames in which there is no stampede while red shading indicates
the frames in which there is a stampede. The arrow indicates the moment when the stampede starts.
Due to the frame characteristics of each dataset, it is necessary to normalise the magnitude
values from the optical flow. This normalisation is performed so that the entropy values are
limited between two close values. This allows the dispersion between the different samples to
be smaller and helps the classifier to detect better. This normalisation is adjusted to the number
of pixels in the image by dividing the magnitude given by the optical flow by a constant that
will be larger as the image size increases. Figure 7 shows an example in which the maximum
value of the entropy is similar to the maximum value in UMN (figure 7).
Figure 7: Green shading indicates the frames in which there is no stampede while red shading indicates
the frames in which there is a stampede. The arrow indicates the moment when the stampede starts.
To observe the improvement of the system performance, we compare the ROC curves of our
system with those provided in the baseline [15]. It is worth note that the ground-truth used
by this paper [15] defines the beginning and the end of the stampede too late in relation to
the actual times of the videos. For this reason we have manually-labelled the videos, using the
above mentioned definition of stampede. Thus, our ground-truth is more accurate and realistic
than the one used by [15]
The first scenario is the UMN lawn (figure 8), in which no new actors enter the scene. Figure
8a shows an example of a frame corresponding to this scenario, whereas figure 8b compares the
ROCs and the AUC for our system (in red) and for the baseline [15] (in blue). Comparing the
ROCs, it can be seen that the results are similar, although some improvement is obtained in our
system.
(b) ROC lawn scenario: red our system and in
(a) Lawn scenario. blue the system [15]
Figure 8: Results for Lawn UMN dataset.
The indoor scenario (figure 9) is characterized for being a recording of a hall, where unusual
activity is observed, such as the entrance and exit of people, as well as much more abrupt light
changes than in the other two scenarios. The indoor scenario is where the most significant
improvement is observed, having important increase in the area under the curve (AUC), being
the colour red our system and in the colour blue, the [15] system (figure 9b).
(b) ROC indoor scenario: red our system and
(a) Indoor scenario. in blue the system [15]
Figure 9: Results for Indoor UMN.
Finally, figure 10 shows the last scenario of the UMN dataset. It is recorded in a plaza, where
the people is walked. This scenario (figure 10a) has the lighting, the size of the people, recording
environment similar to the lawn scenario. For this reason, the values obtained in the ROC curve
(figure 10b) are practically equal to the lawn scenario. As it is shown in the figure the AUC
obtained by our system (colour red) and the system [15] (colour blue) is very similar, having
minor improvements.
(b) ROC plaza scenario: red our system and in
(a) Plaza scenario. blue the system [15]
Figure 10: Results for Plaza UMN.
Regarding the PETS 2009 dataset (figure 11), we find a view similar to the lawn and plaza
scenaios in the UMN dataset. The frames show a street crossing where a group of people run in
a stampede (figure 11a). The AUC obtained with our method provides a better operation than
[15].
As it has been explained before, our proposal is able to perform in real time, being faster
(b) ROC PETS 2009: red our system and in blue
(a) PETS scenario. the system [15]
Figure 11: Results for PETS 2009.
than [15]. In the table 2 is shown a comparison between our model and the [15] model. In the
table there are compared both frame per second processed by each proposal and the AUC value.
It can be seen that our model obtains better results in every field, both in computational cost
(more than 2x speed up) and AUC (with and improvement from +3% to +10%, depending on the
dataset).
Table 2
Comparison of our system with [15]
Our proposal Pennisi et al. [15]
Dataset Scenario
FPS AUC FPS AUC
Lawn 54 0.99 20 0.96
UMN Plaza 54 0.99 20 0.97
Indoor 53 0.98 20 0.88
Street-1 22 0.99 11 0.95
PETS 2009
Street-2 23 0.99 11 0.96
5. Conclusions
In this work, we have proposed an approach for real-time stampede detection in low and
medium density crowd scenarios. The proposal is based on a feature vector extracted from
the optical flow entropy, and this does not require the use of thresholds, since there has been
replaced by a stacking classifier, based on the union of a random forest with ten estimators and
an SVC, that works properly in the different analysed scenarios.
We have selected and extracted features for stampede detection. These features are extracted
through the entropy information given by the optical flow. These features are suitable descriptors
for the detection of the stampede beginning.
The proposal has been evaluated exhaustively in UMN and PETS 2009 datasets, that has been
widely used in stampede detection works, and compared to other state-of-the-art proposals in
terms of accuracy and computational cost. For this evaluation, the UMN and PETS (stampedes)
datasets have been hand labelled, adjusting in a more precise way the start and end frames of
the stampedes. For this reason, our ground-truth is tighter and more precise than the existing
ones.
As conclusion, the system offers an improvement of the results respect to previous state-of-
the-art works both in precision and computational cost. In addition the system does not depends
on a threshold to run correctly and it is able to adapt to different environments. This has been
possible by replacing thresholds with machine learning models that are able to generalise a
solution independently of the environment.
As future lines of work, we plan to develop this system but running on dedicated vision
processing hardware such as GPU or VPU. This will allow a faster response in the processing
and add another layer of parallelisation to the system. Regarding the classification method, it
is proposed to use a dense neural network instead of a classical machine learning model and
compare how the system behaves in deep learning models. In addition, it is intended to test
the system on more datasets in order to evaluate more environments. Evaluating in a more
extensive way the generality of the designed system.
Acknowledgments
The authors would like to thank the GEINTRA research group (geintra-uah.org) for their support
and background work. This research has received funding from the European Union’s Horizon
2020 Research and Innovation Programme under PALAEMON project (Grant Agreement nº
814962), and by the Spanish Ministry of Economy and Competitiveness under project HEIMDAL-
UAH (TIN2016-75982-C2-1-R).
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