Journal of Signal and Information Processing, 2010, 1, 77-81
doi:10.4236/jsip.2010.11008 Published Online November 2010 (http://www.SciRP.org/journal/jsip)
77
A Statistical Framework for Real-Time Traffic
Accident Recognition
Samy Sadek1, Ayoub Al-Hamadi1, Bernd Mic h ae lis1, Usama Sayed2
1Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke-University Magdeburg, Magdeburg,
German y, 2Electrical Engin eer ing department, Assiut University, Asyut, Egypt.
Received October 27th, 2010; revised November 15th, 2010; accepted November 1 8th, 2010.
ABSTRACT
Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine
vision and pattern recognition because of its immense application potential in developing autonomous Intelligent
Transportation Systems (ITS). In this pape r, we presen t a new framework toward a real-time automated recognition of
traffic accident based on the Histogram of Flow Gradient (HFG) and statistical lo gistic r egression analysis. F irst, opt-
ical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the
HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Fi-
nally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental
results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of
higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring ap-
plications.
Keywords: Activity Pattern, Au tomatic Traffic Ac c ident Recogn ition, Flow Gradient, Logistic Model
1. Introduction
Read traffic plays an extremely remarkable role in to-
day's life and many crucial services and human activities
are becoming more dependent either directly or indirectly
on it. Therefore the efficient management for traffic of
road vehicles has become an imperative need today for
any country. In recent traffic management system, traffic
surveillance by means of monitoring cameras has already
been applied [1,2]. However, existing methods predomi-
nantly rely on human observation of captured video se-
quences of images. This needs a great deal of human
effort and time and does not support a real-time resp onse
to abnormal events. In the other hand, current intelligent
traffic surveillance systems based on computer vision
and image processing algorithms, can track, localize, and
recognize vehicles in video sequences captured by road
cameras with little or no human intervention [3]. Fur-
thermore these systems have the ability of analyzing the
vehicle acti vities and giving full a nd precise descrip tions
based on the results of motion detection and tracking
processes. This helps, in turn, in facilitating daily traffic
management and allowing an instant response when ab-
normal events occur, and then an advanced viable sur-
veillance system can be developed.
The purpose of this paper is to investigate the problem
of automatic traffic accident recognition and try to de-
velop a real-time framework for it. The proposed frame-
work performs the following major functions: first, it
detects the accident as it develops. Then, it traces the
accident vehicle and records its trajectory during accident
period. These captured information, recorded by the sys-
tem, are valuable that can provide guidance for investi-
gators in determining accident causes and follow-up ac-
tion. Furthermore, via the abnormal traffic behavior of
vehicle, the system has the ability to predict beforehand
the probability of accident occurrence and give a warning
signal that may assist to avoid the ac c ident.
The rest of the paper is structured as follows. Section 2
presents related literature. In Section 3, the proposed
framework of automatic traffic accident recognition is
described in detail. In Section 4 the experimental results
are reported and the performance of the approach is
compared with that of recently reported techniques. At
last, in Section 5, conclusions are drawn and the future
directions are discussed.

A Statistical Framework for Real-Time Traffic Acci den t Recogn it ion
78
2. Related Work
Over the course of the last few years, researchers in
computer vision and image processing fields have looked
with more interest on traffic accident detection [4,5]. As
a result, several approaches (with varying degrees of so-
phistication and success) have been developed to address
this problem [6-10]. In [11], the authors propose a system
for automatic incident detection. The system aims to dis-
tinguish between different types of incidents. While in
[12] Hu et al. propose a probabilistic model for detecting
traffic accident using fuzzy self-organizing neural net-
work. Additionall y, Kimachi et al. [13] focus their atten-
tion on studying the abnormal vehicle behavior causing
an incident. Their work is based on the concepts of fuzzy
theory. The decision if an accident occurs or not relies on
the behavioral abnormality of some continual image
shots. Ze ng et al. [14] develope a technique for automatic
incident detectio n using D-S evidence theory data fusion
based on probabilistic output of multi-class SVM. How-
ever, the results of most of these methods mentioned
above are still unsatisfactory and further efforts are
needed to develop them.
3. Proposed Approach
In this section, the proposed traffic accident recognition
framework is presented. Our main goal is to develop a
simple fast technique for traffic accident recognition,
which can efficiently operate under real-time constra ints.
To achieve real-time speed, various steps are taken. The
real 24-bit color video images are converted to monoch-
romatic video images. The 640 × 480 digitized image is
averaged and sub-sampled to a resolution of 320 × 240.
The directions of flow gradient are measured using a
simple 8-bin histogram. With these steps, the total pro-
cessing time is 45 ms per frame. Furthermore, we want
the detection process to be relatively tolerant to changes
in lighting conditions. The approach is designed to be
powerful and robust enough, and to yield the best accu-
racy-speed trade-off. An outline of the proposed traffic
accident recognition system is shown in Figure 1.
3.1. Opti ca l F lo w Estimation
The measurement of optical flow is a crucial in the
processing o f video sequence s and is used in performing
a wide variety of tasks. However accurate estimation of
optical flow is a challenging task, which often requires
addressing di fficult ener gy optimization problems. In this
work, optical flow is estimated based on the following
hypotheses (see [15]).
1) Brightness constancy. The brightness of a pixel
does not change as it is tracked from frame to frame.
2) Temporal persistence or “small movements”. The
temporal increments are fast enough compared to the
scale of motion in the image. T his means the object does
no t mo v e m uc h f r om frame to frame .
3) Spatial coherence. Adjacent points belonging to the
same surface in a scene have similar motion, and project
to nearby points on the image plane.
No w consid ering the above -mentioned hypotheses, we
have the following differential equation on the intensity
function
( )
,,I xyt
as follows
( )
,, =0 dIxy t
dt
(1)
Equation (1) can be expanded into the following par-
tial differential equation as follows
=0
Idx Idy I
x dty dtt
∂∂∂
++
∂∂∂
(2)
Figure 1. An outline of the proposed traffic accident rec ognition framework.

A Statistical Framework for Real-Time Traffic Acci den t Recogn it ion
79
Replacing dif fere ntial terms w ith ,, ,,.
xyt
III uv gives
=0
x yt
Iu Iv I++
(3)
Equation (3) can be formed into a matrix equation as
follows
( )
=
T
t
If I∇⋅ −
 
(4)
where =x
y
I
II

∇


 and
=u fv




.
Equation (4) has two unknowns and cannot be solved
as such. To find the optical flow, another set of equations
is needed, which can be obtained by an additional con-
straint. The solution as given by [15] is a non-iterative
method which assumes a locally constant flow. As an
improvement over the original Lucas-Kanade method,
the algorithm is iteratively carried out in a coarse-to-fine
manner, in such a way that the spatial derivatives are first
computed at a coarse scale, and then iterative updates are
successively computed at finer scales.
3.2. Feature Extraction
HOG (Histogram of Oriented Gradient), first proposed
by Dalal and Triggs in 2005 [16], is a feature descriptor
originally used for the purpose of pedestrian detection in
static imager y. This technique counts o ccurre nces of gr a-
dient orientation i n localiz ed p or tions of an ima ge. In this
work, the original HOG algorithm is utilized, but with
some adaption to be appropriate to deal with the flow
field. Such a modified version of HOG is called HFG
(Histogram of flow Gradient). HFG algorithm is very
similar to that of HOG, but differs in that HFG locally
runs on optica l flow field in motion sce nes (see Figure 2
(a)). Furthermore, the imple mentation of HFG is compu-
tationally faster than that of HOG counterpart. T he ma g-
nitude and the angle of the optical flow required to con-
struct HFG are determined by:
( )
22
,=muvu v+
(5)
( )
,= arctanv uv u ϕ



(6)
where
m
and
ϕ
are the magnitude and the angle of
velocity flow respectively .The orientation of flow is
represented by an 8-bin histogram of gradient orienta-
tions in the range of
( )
, ππ −
, as depicted in Figure
2(b).
3.3. Classification
In this section the automatic classification stage of the
proposed system is described, as well as some adapta-
tions are made to allow the system to be robust against
gradual and sudden changes in the scene. By using the
Figure 2. Simple illustration of orientation histogram of
flow (a) flow gradients, (b) 8-bin HFG.
features extracted on the previous step, the location of
the center of gravity for each pattern is given by
( )
1
=i
ij
j
i
h
ςυ
∑ (7)
where
( )
2
i
j
R υ ∈
and i h are the flow vectors belonging
to the pattern
i
and the histogram of that pattern re-
spectively. Then the Euclidean distance metrics (EDMs)
between each two patterns are calculated
iji j
ij
λ ςς =−∀≠ (8)
The distances are then normalized to get a quantitative
parameter upon which classification is determined since
the normalized distance is a characteristic quantity not
easily influenced by sudden changes. Given the norma-
lized distances,
ˆij
λ
between each two patterns
i
and
j
, it is possible to define the probability
ij
p
that de-
termines the relationships between different patterns us-
ing a sigmoidal mapping as follows
( )
2
ˆ
= ==
1
ijijij
pp e
αδ
ς ςλδ
+

(9)
where
δ
is the observed distance value. The parameter
α
is determined using the well-known logistic regres-
sion technique. Statistically speaking, logistic regression
(sometimes ter med logistic mo del or logit model) is used
for prediction of the probability of occurrence of an event
by fitting data to a logistic curve. It is a generalized li-
near model used for binomial regression. Logistic model,
like many fro m of regressio n anal ysis, make s use of sev-
eral predictor variables that may be either numerical or
categorical (for more details see [17]).
The orientation mean is considered and combined with
the probabilities obtained from the above mapping. We
enforce some restrictions on the classificatio n process by
adjusting the pairwise probabilities. For example, the

A Statistical Framework for Real-Time Traffic Acci den t Recogn it ion
80
pattern pair prob ability is set to ze ro if the pattern de nsity
is too small (in the evaluations, ratio 0.2 was utilized as
the limit). The pairwise probabilities obtained is finally
compared with a threshold to determine the state of each
vehicle and then decide whether the accident has oc-
curr ed, i s likel y to occur o r not.
4. Experimental Results
In this section, the experiments undertaken to evaluate
the proposed framework illustrated above are described
and the results that confirm the feasibility of the pro-
posed system are shown. All the algorithms of the pro-
posed system have been implemented using Visual Stu-
dio 2008 and OpenCV and the software has been ex-
ecuted on a Pentium 4 (2.83 GHz) computer running
under Windows Vista platform. Due to the difficulty
(and danger) of capturing or simulating traffic accidents
in real scenes, it was only possible to carry out the expe-
riments in a relatively limited number of a real traffic
accident scenes. The proposed framework has been
tested on a set of 45 video streams depicting a total of
over 250 real scenes of traffic accidents or abnormal ve-
hicle events captured by traffic surveillance cameras. All
these data were collected from Internet sites and supplied
free of charge. Data comprise of a wide variety of dif-
ferent road types such as a straight roads, curves, ramps,
crossings, and bridges, and also many vehicle events
incl udin g tur ning l eft, tur ning right , e nteri ng, and leaving
were involved in. In order to quantitatively evaluate the
performance of the proposed framework in recognizing
traffic accident, we have used a receiver operating cha-
racteristic (ROC) curve that defines the relationship be-
tween the detection rate (DR) and the false-alarm rate
(FAR), which are given by
#
= 100%
#
correctlyrecognizeda ccidents
DR Total ofaccidents×
#
= 100%
#
falselyrecognizedaccidents
FAR Totalofnon- accidents×
The symbol # used above means “the number of”.
Figure 3 shows the interpolated ROC curve of the pro-
posed recognition system. According to Figure 3, the
performance of the proposed system is very promising.
The recognition rate of the system reaches up to 99.6%
with false alarm rate at 5.2%. What is more, this perfor-
mance meets or exceeds those of recently reported me-
thods [14,9] in terms of recognition rate and false alarm
rate. The example shown in Figure 4 illustrates how the
proposed system could successfully recognize the occur-
rence of a highway traffic accident and then track out and
record the trajectory of the vehicle by which the accident
was occasioned. Finally, the proposed accident recogniz-
er can run comfortably at 25 fps on average (using a 2.8
GHz Intel dual core machine with 4 GB RAM running
Microsoft Windows Vista). This timing performance
enables our method to provide delay guarantees to
real-time surveillance and traffic monitoring applica-
tions.
Figure 3. ROC curve for the proposed automatic traffic
accident recognition framework.
Figure 4. An example of a highw ay traffic accident recognition. The first row depicts three camera shots of the accident ve-
hicle, (a) before the accident, (b) beginning the accident, and (c) end of the accident. The second row shows the clustering
results.

A Statistical Framework for Real-Time Traffic Acci den t Recogn it ion
81
5. Conclusions and Future Work
In this paper, a new framework for real-time automated
traffic accident recognition has been introduced. The
framework is based on flow gradient histogram and sta-
tistical logist ic regression. T he results of the e xperiments
conducted have validated the effectiveness and efficiency
of thi s fra mewor k and it matc hes or outperforms the best
reported results on automatic traffic accident re cogni tio n.
Future work will be along two main axes. The first will
be the further improvement of the classification stage by
using advanced machine learning algorithms that help in
improving the overall recognition efficiency of the pro-
posed approach. The second will look at the possibility
of applying the approach to other applications that con-
sider spatio-temporal features, such as human event rec-
ognition, crowd behavior analysis, tracking of an indi-
vidual in crowded scenes, etc.
6. Acknowledgements
This work is supported by Transregional Collaborative
Research Centre SFB/TRR 62 “Companion-Technology
for Cognitive Technical Systems” funded by DFG, and
BMBF Bernstein-Group (FKZ: 01GQ0702). The doctor-
al scholarship provided by University of Sohag (Egypt)
to the first author is also gratef ully acknowledged .
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