Region-based Abnormal Motion Detection in Video Surveillance

Jorge Henrique Busatto Casagrande

and Marcelo Ricardo Stemmer

ucleo de Telecomunicac¸

oes, Instituto Federal de Santa Catarina, IFSC Campus S

ao Jos

Rua Jos

e Lino Kretzer, 608, CEP 88103-310, S

ao Jos

e, Santa Catarina, Brasil

Departamento de Automac¸

ao e Sistemas, Universidade Federal de Santa Catarina, DAS/UFSC,

CEP 88040-900 Caixa Postal 476, Florian

opolis, Santa Catarina, Brasil

Keywords:

Abnormal Motion Detection, Video Analysis, Automated Surveillance, Motion Analisys, Pattern Recognition.

Abstract:

This article proposes a method to detect abnormal motion based on the subdivision of regions of interest

in the scene. The method reduces the large amount of data generated in a tracking-based approach as well

as the corresponding computational cost in training phase. The regions are spatially identiﬁed and contain

data of transition vectors, resulting from the centroid tracking of multiple moving objects. On these data, we

applied a one-class supervised training with one set of normal tracks on Gaussian mixtures to ﬁnd relevant

clusters, which discriminate the trajectory of objects. The lowest probability of transition vectors is used as

the threshold to detect abnormal motions. The ROC (Receiver Operating Characteristic) curves are used to

this task and also to determinate the efﬁciency of the model for each size increment of the region grid. The

results show that there is a range of grid size values, which ensure a best margin of correct abnormal motions

detection for each type of scenario, even with a signiﬁcant reduction of data samples.

1 INTRODUCTION

In the last years, computer vision systems started

to have an important contribution in capturing rele-

vant information from scenarios and targets in video

surveillance, since images taken by cameras have

many similarities with the human vision. The in-

formation captured in this way help computer sys-

tems to make decisions about what they are “watch-

ing”, similar to the human behavior. In that sense,

one of the prominent applications of vision systems

is the tracking of moving objects and inference about

their behavior (R

aty, 2010). The research in video

surveillance around autonomous or automated sys-

tems seeks strategies that can improve results in pat-

tern recognition and target behavior. The approaches

generally prioritize ideas that require lower computa-

tional cost, in order to make feasible applications in-

volving real-world scenarios and in different contexts

(Sodemann et al., 2012). According these authors,

effective approaches for motion analysis are point-

ing to spatio-temporal probability models. The in-

herent uncertainty of the observations in video scenes

is a characteristic problem, which reinforces the use

of probabilistic reasoning in the events modeling.

Therefore, the most common machine modeling for-

malisms adopt Bayesian networks, HMM (Hidden

Markov Models) and GMM (Gaussian Mixture Mod-

els) including their variations. For pattern recogni-

tion and training of these models, statistical learning

methods such as the EM algorithm (Expectation Max-

imization) and kernel-based methods such as SVM

(Support Vector Machines) are prevalent (Zeng and

Chen, 2011), (Bishop, 2006).

1.1 Related Work

The authors who venture to design complete propos-

als, from the capture of video frames up to the be-

havior analysis of moving objects, need to determine

constraints on their models in order to make feasi-

ble the computation of the heavy workload involved

in every process (Berclaz et al., 2008), (Basharat

et al., 2008), (Li et al., 2012), (Jiang et al., 2011).

The works of these authors and (Ermis et al., 2008),

(Kiryati et al., 2008), (Shi et al., 2010), (Hanapiah

et al., 2010), (Feizi et al., 2012), (Haque and Murshed,

2012), (Cong et al., 2013) are generally focused on

the search for better results on a set of standard video

datasets created in their own trials or adopted from

research groups around the world. Some approaches

also deal with real world scenes, but are generally lim-

ited in ﬂexibility in what concerns scenarios, targets,

video length and reality. In addition, the use of heuris-

710

Busatto Casagrande J. and Ricardo Stemmer M..

Region-based Abnormal Motion Detection in Video Surveillance.

DOI: 10.5220/0004846607100717

In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM-2014), pages 710-717

ISBN: 978-989-758-018-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

tics is very common to simplify the modeling. Most

of these papers are in another approach category into

abnormal motion analysis: the motion-based. This

category is attractive because it requires no prepro-

cessing of video in opposite with tracking-based cat-

egory which the robust tracking of multiple objects is

still an open problem.

Previous research works have used the sub divi-

sion of the region of interest in the scene in order

to render processing more efﬁcient, as well as meth-

ods to reduce dimensionality and computational cost.

The so-called curse of dimensionality evaluated by

(Bishop, 2006) is a recurring theme that requires a

more sophisticated approach on n-dimensional data

when n is greater than 3. The authors (Tziakos et al.,

2010) used a grid of subregions as local abnormal

motion detectors to test the effects of dimensionality

reduction in unsupervised learning fashion. Authors

such as (Elhoseiny et al., 2013) have consensus that

it is necessary to use these techniques in combination

with more robust and simple to implement classiﬁers.

Otherwise it is impracticable to apply their ideas in

the real world. We have adopted a scene modeling

method similar to the one proposed in the framework

implemented by (Li et al., 2012). However, the size of

the grid regions in our case, isn’t determined empiri-

cally. In the work of (Feizi et al., 2012), the number

of pixels in the cluster size is conveniently determined

by their method. Already the work of (Kwon et al.,

2013) used the concept of entropy to adjust the size

of the regions, which they named cell in order to tune

the best data arrays that detect abnormal motions.

The authors (Basharat et al., 2008) developed a

pixel-based method to identify abnormalities in both

local and global motions

using GMM over a set of

transition vectors stored at each pixel position. They

noticed that the capture of transition vector data only

from the centroid position of the objects made the mo-

tion modeling spatially sparse. This wasn’t suitable

for their learning model. Then, in each position occu-

pied by moving objects, they copied the same vectors

in all neighboring pixels up to the limit of the bound-

ing box area of objects. The authors used a dataset

with 1342 tracks that created a exponential density of

samples. This helped to reduce the sources of errors

in the learning model, but created computational con-

straints. The number of observable transitions deﬁned

as τ, had to be ﬁxed at the limit of τ = 20 to make the

model computationally tractable.

According to the authors, local motions are those ana-

lyzed in the transition immediately after the current position

of the moving object. The following transitions are associ-

ated with the global motion.

1.2 Problem Description

This article focuses on the problem of the compu-

tational load required in pattern recognition of the

tracking-based approaches, speciﬁcally when statisti-

cal models are adopted in training phases. In contrast

to pixel-based solutions, this work presents a region-

based method that aims to reduce the dimensionality

of the involved data and maintains the inference qual-

ity of the abnormal motions detections.

Statistical models continue earning spaces to con-

tribute to the solution of problems that emerge as chal-

lenges in the various processes of video analysis, in

any category approach. The large amount of data re-

quired to be processed and the sophistication of the

algorithms are presented as a barrier or even imped-

iment for the computational treatment. (Shi et al.,

2010) comment that real-time tracking-based cate-

gory of all moving objects in complicated scenes is

too difﬁcult to achieve in real-world situations. We

want to show that, adopting our method, this isn’t a

problem.

2 PROPOSED APPROACH

This paper intends to analyze changes in moving ob-

jects between regions larger than a single pixel of

video resolution. As (Kwon et al., 2013), we under-

stand that small displacements of the object centroid

around a neighborhood of pixels has very little inﬂu-

ence on the evaluation of the motion. Thus, ignoring

the data portion representing these small movements,

we will not signiﬁcantly change the overall conclu-

sion on the motion abnormality.

2.1 Assumptions

The analysis of abnormal motion is the last step of

a tracking-based framework. Then, we concentrated

only in this task isolating it from other processes. To

take us faster the objectives, other assumptions and

tools also were considered:

Motion, Scene and Learning Models. We adopted

the similar models proposed by the authors (Basharat

et al., 2008) applying however, the strategy of region-

based analysis rather than pixel-based.

LOST Dataset. We used three sets containing 8

to 10 sequences of 30 minutes of the LOST Project

videos (Longterm Observation of Scenes with Tracks

Dataset) available by the authors (Abrams et al.,

2012) at http://lost.cse.wustl.edu. The LOST dataset

comprises several videos made from streaming of out-

door webcams, captured and organized by numbers

Region-basedAbnormalMotionDetectioninVideoSurveillance

711

(1 to 25) in the same half hour every day at vari-

ous locations around the world. The dataset contains

metadata geolocation, object detection and tracking

results. This dataset, among a number of surveyed

such as (Oh et al., 2011) met the expectations of our

work especially because it provides video annotations

of objects tracking in different types of scenarios. We

improve the tracking quality, selecting the best tracks

and performing complementary annotations on video.

Off-line and Supervised Training. The GMM

model was trained by the EM algorithm us-

ing the MATLAB functions emgm.m and vbgm.m

(http://www.mathworks.com/matlabcentral). These

functions implement the EM algorithms proposed by

(Bishop, 2006) which it behaved better in accuracy

and speed when compared to others proposed by

(Figueiredo and Jain, 2002). These are alternatives to

make the use of EM stable, bypassing the drawbacks

when used in the standard form. The authors present

algorithms that are able to select the number of com-

ponents (clusters) automatically (unsupervised) and

without the need for care in the initialization of ﬁnite

mixture models belonging to multivariate data.

Homografy. Because the video sequences are cap-

tured from real video surveillance cameras outdoor, it

becomes impractical to perform the homography, or

perspective transformation through a camera calibra-

tion algorithm. Anyway, a real sense of depth and size

or volume of objects in our analysis is irrelevant in-

formation since we are only interested in the centroid

position in each transition track of the object;

Computational Cost Metric. The proposed method

was implemented in MATLAB using a computer

Pentium Intel

Core

i5 CPU M450@ 2.40GHzx4,

6GiB RAM and operating system Ubuntu 12.04LTS

64-bit. The measure of the computational cost, and

rely on computational resources is sensitive to the

structure of the algorithm adopted in the implemen-

tation of the method. Therefore, since there is a math-

ematical relationship between the computational cost

with the number of samples involved in the process,

we understand it is sufﬁcient to use the total samples

as a metric to quantify and compare the results. Un-

like our approach, the time complexity is much more

important to motion-based approaches because of the

processing of each frame subregion is continuous. In

our case, once the model is trained, the decisions are

computed in O(M) where M depends on τ and the

number of moving objects in the frame. Otherwise, in

motion-based, (Haque and Murshed, 2012) highlights

that their complexity isn’t higher than the pixel-based

background subtraction process. (Shi et al., 2010)

concludes with Θ(N

logN) for all their processes and

N is the our p

equivalent;

Annotated Dataset. We have classiﬁed the main

types of objects and global motion on LOST dataset

according Tables 1 and 2. These complementary an-

notations, turn the dataset an equivalent robust track-

ing step. Thus, we deﬁned and left at disposition to

use in simulations a 7-dimensional vector for each

sample containing: 2D centroid coordinates in the

frame resolution, the width and height of the bound-

ing box, the timestamp of the transition, the class

types of object and the global motion;

Table 1: Some object class types observed and annotated.

object type class (v) description

1 1 person

4 2 persons group

7 bike

8 motorcycle

10 car/SUV

14 bus

Table 2: Object motion class types observed and annotated.

motion type class description

0 Normal - usual path

1 Abnormal - unusual path

2 Abnormal - unusual local

3 Abnormal - unusual object

Dimensionality Reduction. We used for the pur-

pose of training the model, only 3 of the 7 dimen-

sions available in the dataset: the number of the re-

gion where the centroid is, the type of object and its

timestamp in frame. The reduction of dimensions pre-

sented deviates our proposal from the curse of dimen-

sionality problems. Obviously in a real application it

would be necessary include in our proposal, both a ro-

bust tracking and a classiﬁer that separates the object

and motion types in multi-classes;

Abnormal Tracks. The video sequences of real

scenes can not freely include abnormal tracks to form

the test set to evaluate our method. However few

inconsistencies contained in the LOST dataset, due

to the simplicity of the tracking algorithm, are pur-

posely held to be considered as motion abnormalities

in the training phase. Examples of these anomalies

are the tracking sudden changes that occur from oc-

clusion between mobile objects. In this case, a track

which starts associated with a car, can ﬁnish associ-

ated with a person from a occlusion region between

them. This type of abnormality is labeled in video an-

notation with the number “1” from Table 2. In our

implementation, we represented the motion and ob-

jects types by numbers placed above the bounding

box. Figure 1 shows this detail.

ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods

712

Figure 1: Sample of three objects in LOST video #1 using

labeling according to Table 2 (above and left) and Table 1

(above and right). The white number represents the track.

2.2 Scene Modeling

Our idea is to cluster data in each region where the

centroid of each object is observed. This region is a

square area with side measuring p

pixels which is

deﬁned here as grid factor and p

∈ N

∗

. Then a ﬁxed

grid is deﬁned with regions having a resolution equal

to or less than the resolution of the video frame. As-

suming the resolution frame R ×C pixels, the regions

}

p=1

, are numbered sequentially from left to right

and top to bottom turning the grid into a unidimen-

sional vector of regions where g = dR/p

e.dC/p

In this respect, the two dimensions representing the

position of the centroid of an object is reduced to a

scalar that represents a position in the vector regions.

2.2.1 Grid Formation

The p index of the region r

where the object cen-

troid is located, can generically be determined by the

expression p = b(x

− 1)/p

c.dC/p

e + dy

e, ac-

cording to the current 2D centroid position (x

)

were {x

}

and {y

}

. The region deﬁned as r

set {r

}

p=1

is the ﬁrst set of pixels from the top left

of the frame. The region r

is immediately to the right

of r

and so on up to the end of the columns or rows

of the frame pixels. If C or R isn’t a multiple of the

size of p

, the last regions on the rigth or bottom will

have dimensions smaller than p

but even so, they are

labeled sequentially in the grid. Figure 2 illustrates

an example of the grid regions r

using a grid factor

= 39. The total area of the frame is transformed

into a one-dimensional vector with g = 221 elements.

For each frame, each object with respective 2D cen-

troid coordinates, is associated with a single region

even if the areas of the bounding box are overlapping

the neighboring regions.

Figure 2: Labeling of grid formation over a generic frame

of LOST video #17 with R ×C = 480 ×640 pixels.

2.3 Motion Modeling

Each grid region is used as a reference to store all

data vectors of a window of τ following object tran-

sitions. Obviously, the same region may belong to

other object paths, and thus, accumulating an increas-

ing amount of data. The transitions window deﬁnes

how long the track of the object should be observed.

The higher the value of τ, more speciﬁc becomes the

analysis. The assumption to be a transition is consid-

ered when an object jumped to a different region in

the next observation. Each track is observed within

the limit of the window.

The annotated dataset offers a set of n tracks T for

each video is represented as {T

}

i=1

and k ∈ N

∗

the set of frames k where the object is sampled. Each

frame k has a well deﬁned timestamp t in the video

and t ∈ R

. Then T

represents a set of m observa-

tions of the same object, T

= {O

}

j=1

. Each obser-

vation is a set of transition vectors O

= {γ

j+a

}

a=1

were γ

j+a

= (r

,v,t)

is a vector transition sampled

the trail and that contains the temporal continuous

record t (timestamp) of object type v, in region r

of the grid. Figure 3 shows these future transitions

observation of any object in frame k in a sampling

window up to τ. All transition vectors up to γ

j+τ

are

associated as samples at the region in the observation

point O

Figure 3: Detail of the motion model of a track T

proposed

by (Basharat et al., 2008) and adapted by us.

Region-basedAbnormalMotionDetectioninVideoSurveillance

713

2.4 Learning Modeling

To build the database for training model, it is neces-

sary the long-term observation of the scenes in order

to obtain a sufﬁcient samples of the objects types and

their displacements. Therefore, we use three videos

with different sizes according to Table 3. The charac-

teristic of the scenarios and resolutions involved were

purposely chosen. Video #1 has a more sparse num-

ber of tracks than the others. Video #14 has most of

its tracks concentrated in speciﬁc regions of the scene.

Figure 4 shows the detail of the distribution of tracks

and samples of each video. The locations of pixels

with more intense colors are those with the largest

number of samples. The dispersion observed in sam-

ples videos #1 and #17 suggests that many areas may

have insufﬁcient data for training of the GMM.

Table 3: Data of the video datasets corresponding values

achieved after training steps due to a factor grid p

= 1.

LOST dataset #1 #14 #17

resolution 480x640 240x320 480x640

hours 4 4 5

anormal tracks 37 32 116

normal tracks 1190 1755 2990

transitions 54661 58072 115688

samples 816018 777044 1646275

Figure 4: Scenarios samples used according to the LOST

datasets. In (a) video #1, video #14 and video #17, in this

order, In (b), the corresponding normalized distributions.

Any deviation of usual local or global motion, re-

sults in signiﬁcant differences when calculating the

probability and abnormalities are so identiﬁed. As

an example, a person riding a motorcycle who moves

on a trajectory and speed of usual pedestrians, should

only be identiﬁed as an object with abnormal motion

if it is on a sidewalk.

An iterative process conducted in off-line mode is

done to ﬁnd the value or range of values of p

which

leads to the best performance in the identiﬁcation of

abnormal motions. In the ﬁrst step of training, all

tracks annotated as abnormal, are excluded from the

dataset. The sampling for each region is performed

according motion model. Therefore, this method

refers to a supervised learning since the training data

consists of only one class normal events (Sodemann

et al., 2012). In the second step, the dataset contain

normal and abnormal events so that all tracks have an-

notations to be used as targets in lifting ROC curves.

The found threshold represents the lowest probability

of all transitions sampled in the scenario. Considering

the dimensionality of data clusters equal 3, in sum-

mary, the probability is determined through equation

(1), where η

represents the samples quantity in each

region r

and a = {1,2,...τ}.

P(γ

j−a

|(Σ,µ)

) =

(2π)

|Σ|η

exp

−

(Σ−µ)

−1

(Σ−µ)

(1)

In each iteration we saved into vectors, the amount

of samples used in the training steps and the threshold

value associated with the best ROC curve efﬁciency.

The value of p

is incremented by one from the uni-

tary value. After some experiments, our simulations

are stopped for the value of p

= 30. The limit of p

increment and the metric ROC efﬁciency is discussed

in section 3.

At the end of this process, one of the p

values and

respective best threshold ROC curve associated can

be adopted for the monitored scenario. The known

threshold is going to be used as a single-class classi-

ﬁer until the necessity of another round. Since both

and respective decision threshold values is chosen,

any size or video sequence in the same video scenery

which contains the annotations on its tracking, can be

tested. One off-line round can be summarized in the

following pseudo-code:

INITIALIZATION; pumax=30; tau=20; targets;

FOR pu=1 TO pumax

\\ 1st Training Step with dataset one-class

r(p)={}: p=1 TO ALL grid regions g;

FOR all n tracks in each j transition

JOIN in r(j+a),[r(j),v(j),t(a-j)],a=1:tau;

FOR p=1 to g

RUN EM over Gaussian Mixtures in r(p);

SAVE learned parameters sigma_p,mu_p,k_p;

END

\\ 2nd Training Step with full dataset

FOR all n tracks in each j transition

out_n = estimate min probability in r(j)|p

from tau previous transitions;

END

RUN ROC curve from (out,targets)_n vector;

threshold_n=ROC efficiency metric;

PLOT samples_used and threshold_n by pu;

END

FIND best(pu,threshold) ranges among all plots;

END

ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods

714

(a) video #1. (b) video #14.

Figure 5: Effects of the increased grid factor over the amount of samples and the performance in abnormal motion detection.

On a test model, for each new position of each

object in each frame and using equation (1), it is esti-

mated the probability of that type of object to be at the

current position and time, originating from each of the

τ previous transitions (high order analysis). If any of

the τ probabilities is less than the used threshold, then

the object is identiﬁed as describing an unusual tra-

jectory from that point until the end of its trajectory

tracking in video. In our implementation, we high-

light in red color the bounding box of the object that

had its motion identiﬁed as abnormal.

3 RESULTS

In the context of this work, we have compiled the

main data results through the curves shown in Fig-

ure 5. The curves show the relationship between the

number of involved samples (red curves) and the ROC

efﬁciency metric for each p

value. For them, a tran-

sition windows τ = 20 was adopted.

Since we are only interested in the highest hit

rate of true positives (T PR) and the lowest hit rate of

false positives (FPR), we adopted as reference met-

ric the ROC efﬁciency through equation 2. (Powers,

2011) suggests a goodness performance measure for

(T PR −FPR), called informedness. A number closer

to 1, indicates better correct ratio for both abnormal

and the normal tracks. The value of ε represents the

number of lost tracks, which is explained hereafter.

ROCe f f iciency = (T PR − FPR).(1 −

total tracks

)

(2)

As an example, the curve in Figure 5(c) shows that

the best ROC efﬁciency value occurs when p

= 7.

The asterisk character presents the best outcome of

the equation 2. Figure 5(d) shows the detail of best

ROC curve and corresponding threshold value.

The numbers alongside different color segments

plotted in the ROC efﬁciency curve, represent the

amount of tracks that have been lost for two reasons:

(i) the number of samples in all object transition re-

gions wasn’t enough for the convergence of the GMM

training algorithm (usually the clusters require at least

30 samples) and (ii) lack of transitions between re-

gions. In our tests, the hypothesis (i) was most rep-

resentative. These losses are well demonstrated in

videos #1 and #17 due to fact that they have a sam-

ple dispersion in many regions of interest, as shown

in Figure 4 (b). The hypothesis (ii) becomes relevant

for larger p

values. Higher values of p

decreases

the transitions amounts. These larger regions can con-

Region-basedAbnormalMotionDetectioninVideoSurveillance

715

tain most or all samples of a shorter track. This was

the reason why we adopt the limit p

= 30, because

from that value on, the loss of tracks get larger. When

tracks are lost, they affect TPR and FPR ratios and

become unequal in results comparison. To avoid this

negative inﬂuence, we apply a penalty factor accord-

ing to equation (2) which takes into consideration the

loss of ε tracks in relation to the total tracks.

We consider an optimal range of grid factors p

for each LOST video. This range must have a min-

imum track loss and must not be less than the value

when p

= 1. The boundary of this range in Fig-

ure 5 is represented by the horizontal dashed line. It

is also clear that in all tested videos there is a com-

mon performance improvement when 1 . p

. 10.

In this range, the number of samples decreases up to

∼60% if compared with equivalent pixel-based mod-

els (when p

= 1). This is the case of video #17 were

the number of samples starts in ∼1.64 milion when

= 1 and decreases to 648,483 when p

= 9.

If we extend the comparison with the previous ap-

proach presented by (Basharat et al., 2008), the dif-

ference is huge due to the motion model these au-

thors makes sample copies in all pixels of bounding

box boundary. In a simulation using the dataset avail-

able by the authors, with video resolution 240x320

pixels and ∼3 hours length, we observed more than

250 million samples and the ROC curve with much

lower performance than we present in Figure 5(d).

The training time was dependent on the video ac-

cording to the samples concentration per region. So,

considering the best value of p

, all videos had their

training time much lower than total time of the videos.

The input vector for the space-time motion model

is reduced to a 3-dimensional space with (r

,v,t). The

distribution of the data vectors in hyperplanes tends to

be sparse. This harms the accuracy and convergence

of the GMM models. Otherwise, too many samples,

or oversampling form a lot of less representative clus-

ters which require more unnecessary computational

effort. In videos #1 and #17 it is notable the fast ef-

ﬁciency increasing from p

= 1. This is an expected

effect because the increasing of regions area, samples

will be adding in these regions and help to ensure or to

improve the clusters during GMM training. This be-

havior isn’t observed with video #14 due to the higher

concentration of samples in a small area in the cen-

ter of the video even when p

= 1 (see detail in Fig-

ure 4 (b)). When p

> 1 an oversampling occurs and

this saturation has not produced good results in the

GMM training. The behavior leads to the conclusion

that region-based models do not require many sam-

ples, however they need to be better distributed.

4 FURTHER WORKS

The method has shown insensitivity to scene context

and low dependency on the robustness of the tracking

algorithm. The uniform behavior of the performance

curves revels these tendency, since they deal with dif-

ferent resolutions, areas of interest, object’s bounding

box ﬁdelity and their tracking, number of track tran-

sitions, number of tracks and video length. In this

aspect, the LOST project opens opportunities for re-

search in scene analysis involving long-term surveil-

lance in outdoor environments. We intend to use it

also for future studies, including the application of the

same method presented here, but using others statisti-

cal models such as HMM.

We observe that the p

range values which leads

to the best performance of the method, tend to match

with the smallest object size tracked (high or width).

These information did not explicitly participate in the

training. Currently, we are engaged in evidence and

theoretical explanation of this trend.

We intend to continue the study of this approach

using a mobile grid rather than a ﬁxed one, as well as

using algorithms like superpixel segmentation.

Alternatively, since the computational effort is re-

duced due to the lower amount of samples, it is possi-

ble to keep a training window in on-line mode. Sim-

ply replacing old samples and repeating the GMM

model training only for updated regions.

5 CONCLUSIONS

We present a new method for abnormal motion de-

tection in real video surveillance scenes. We comple-

mented with video annotations the preexisting tracks

of the LOST dataset. The proposed region-based

method supported by ROC curves, used scene, mo-

tion and learning models focused on dimensionality

reduction to decrease the computational effort with-

out sacriﬁcing performance in detecting abnormali-

ties. Our method avoids the excessive data and so-

phisticated algorithms used in many pixel-based ap-

proaches. In addition, appearance models of objects

did not need to be deﬁned, like most of the region-

based strategies.

Our results show that the method is useful and also

shown good behavior for different scenarios, contexts,

quantity and quality of samples. They have demon-

strated that there is a range of grid factor values that

maintain the efﬁciency in motion analysis, even with

a signiﬁcant reduction in the number of samples used

to train a statistical model such as the GMM.

ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods

716

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