www.ijatir.org
ISSN 2348–2370
Vol.09,Issue.02,
February-2017,
Pages:0260-0267
Copyright @ 2017 IJATIR. All rights reserved.
Hybrid Model Based on Color, Shape and Motion Detection for Video
Surveillance by Raspberry-Pi AKULA PRASHANTH
1, K. RAMBABU
2
1PG Scholar, Dept of ECE, Kasireddy Narayan Reddy College of Engineering and Research, Hyderabad, TS, India.
2Associate Professor, Dept of ECE, Kasireddy Narayan Reddy College of Engineering and Research, Hyderabad, TS, India.
Abstract: Fire causes irreversible damage to fragile natural
ecosystems and greatly affects the socio-economic systems
of many nations especially in the tropics where forest fires
are more prevalent. Early detection of these fires may help
reduce these impacts. Conventional point smoke and fire
detectors are widely used in buildings. They typically detect
the presence of certain particles generated by smoke and fire
by ionization or photometry. Alarm is not issued unless
particles reach the sensors to activate them. Therefore, they
cannot be used in open spaces and large covered areas.
Video based fire detection systems can be useful to detect
fire in large auditoriums, tunnels, atriums, etc. The strength
of using video in fire detection makes it possible to serve
large and open spaces. In addition, closed circuit television
(CCTV) surveillance systems are currently installed in
various public places monitoring indoors and outdoors. Such
systems may gain an early fire detection capability with the
use of fire detection software processing the outputs of
CCTV cameras in real time.
Keywords: Video Surveillance, Fire Detection, Multi Expert
System. I. INTRODUCTION
Image processing is a method to convert an image into
digital form and perform some operations on it, in order to
get an enhanced image or to extract some useful information
from it. It is a type of signal dispensation in which input is
image, like video frame or photograph and output may be
image or characteristics associated with that image. Usually
Image Processing system includes treating images as two
dimensional signals while applying already set signal
processing methods to them. t is among rapidly growing
technologies today, with its applications in various aspects of
a business. Image Processing forms core research area within
engineering and computer science disciplines too. The two
types of methods used for Image Processing are Analog and
Digital Image Processing. Analog or visual techniques of
image processing can be used for the hard copies like
printouts and photographs. Image analysts use various
fundamentals of interpretation while using these visual
techniques. The image processing is not just confined to area
that has to be studied but on knowledge of analyst.
Association is another important tool in image processing
through visual techniques. So analysts apply a combination
of personal knowledge and collateral data to image
processing. Digital Processing techniques help in
manipulation of the digital images by using computers. As
raw data from imaging sensors from satellite platform
contains deficiencies. To get over such flaws and to get
originality of information, it has to undergo various phases
of processing. The three general phases that all types of data
have to undergo while using digital technique are Pre-
processing, enhancement and display, information
extraction.
In the last years several methods have been proposed,
with the aim to analyze the videos acquired by traditional
video surveillance cameras and detect fires or smoke, and the
current scientific effort focused on improving the robustness
and performance of the proposed approaches, so as to make
possible a commercial exploitation. Although a strict
classification of the methods is not simple, two main classes
can be distinguished, depending on the analyzed features:
color based and motion based. The methods using the first
kind of features are based on the consideration that a flame,
under the assumption that it is generated by common
combustibles as wood, plastic, paper or other, can be reliably
characterized by its color, so that the evaluation of the color
components (in RGB, YUV or any other color space) is
adequately robust to identify the presence of flames. This
simple idea inspires several recent methods: for instance, in
fire pixels are recognized by an advanced background
subtraction technique and a statistical RGB color model: a
set of images have been used and a region of the color space
has been experimentally identified, so that if a pixel belongs
to this particular region, then it can be classified as fire. The
introduction of the HSI color space significantly simplifies
the definition of the rules for the designer, being more
suitable for providing a people-oriented way of describing
the color.
A similar approach has been used in [6], where a
cumulative fire matrix has been defined by combining RGB
color and HSV saturation: in particular, starting from the
assumption that the green component of the fire pixels has a
wide range of changes if compared with red and blue ones,
this method evaluates thes patial color variation in pixel
values in order to distinguish non-fire moving objects from
AKULA PRASHANTH, K. RAMBABU
International Journal of Advanced Technology and Innovative Research
Volume. 09, IssueNo.02, February-2017, Pages: 0260-0267
uncontrolled fires. In this paper we propose a method able to
detectfires by analyzing the videos acquired by surveillance
cameras. Two main novelties have been introduced: first,
complementary information, respectively based on color,
shape variation and motion analysis, are combined by a multi
expert system. The main advantage deriving from this
approach lies in the fact that the overall performance of the
system significantly increases with a relatively small effort
made by designer. Second, a novel descriptor based on a
bag-of-words approach has been proposed for representing
motion. The proposed method has been tested on a very
large dataset of fire videos acquired both in real
environments and from the web. The obtained results
confirm a consistent reduction in the number of false
positives, without paying in terms of accuracy or renouncing
the possibility to run the system on embedded platforms.
II. EXISTING AND PROPOSED METNODS
A. Existing Method
In general, the use of flame detectors is restricted to "No
Smoking" areas or anywhere where highly flammable
mater1als are stored or used. Existing method followed the
rules for filtering fire pixels in the HSI color space. This
simple idea inspires several recent methods: for instance, fire
pixels are recognized by an advanced background
subtraction technique and a statistical RGB color model: a
set of images have been used and a region of the color space
has been experimentally identified, so that if a pixel belongs
to this particular region, then it can be classified as fire. The
common limitation of the above mentioned approaches is
that they are particularly sensitive to changes in brightness,
so causing a high number of false positive due to the
presence of shadows or to different tonalities of the red.
B. Proposed Method
Flame detectors are generally only used in high hazard
areas such as fuel loading platforms, industrial process areas,
hyperbaric chambers, high ceiling areas, and any other areas
with atmospheres in which explosions or very rapid fires
may occur. Flame detectors are "line of sight" devices as
they must be able to see" the fire, and they are subject to
being blocked by objects placed in front of them. However,
the infrared type of flame detector has some capability for
detecting radiation reflected from walls. In this paper we
propose a method able to detect fires by analyzing the videos
acquired by surveillance cameras. Two main novelties have
been introduced: first, complementary information,
respectively based on color, shape variation and motion
analysis, are combined by a multi expert system. The main
advantage deriving from this approach lies in the fact that the
overall performance of the system significantly increases
with a relatively small effort made by designer. Second, a
novel descriptor based on a bag-of-words approach has been
proposed for representing motion. The existing system uses
only contrast based approach. It does not give efficient
result. It takes long time identification and also the result is
not accurate. The purpose of the System Analysis is to
produce the brief analysis task and also to establish complete
information about the concept, behavior and other
constraints such as performance measure and system
optimization. The goal of System Analysis is to completely
specify the technical details for the main concept in a concise
and unambiguous manner. The package selected to develop
watermarking is MATLAB and the package has more
advanced features. As the system is to be developed in
Watermarking, MATLAB platform with windows
Application is preferred.
III. LITERATURE SURVEY
Fire And Smoke Detection In Video With Optimal
Mass Transport Based Optical Flow And Neural
Networks.Kolesov, P.Karasev, A.Tannenbaum .E.Haber:
Detection of fire and smoke in video is of practical and
theoretical interest. In this paper, we propose the use of
optimal mass transport (OMT) optical flow as a low-
dimensional descriptor of these complex processes. The
detection process is posed as a supervised Bayesian
classification problem with spatio-temporal neighborhoods
of pixels;feature vectors are composed of OMT velocities
and R,G,B color channels. The classifier is implemented as a
single-hidden-layer neural network. Sample results show
probability of pixels belonging to fire or smoke. In
particular, the classifier successfully distinguishes between
smoke and similarly colored white wall, as well as fire from
a similarly colored background. A Probabilistic Approach
for Vision-Based Fire Detection in Videos Paulo Vinicius
Koerich Borges, Member, IEEE, and EbroulIzquierdo,
Senior Member, IEEE: Automated fire detection is an active
research topic in computer vision. In this paper, we propose
and analyze a new method for identifying fire in videos.
Computer vision-based fire detection algorithms are usually
applied in closed-circuit television surveillance scenarios
with controlled background.
In contrast, the proposed method can be applied not only
to surveillance but also to automatic video classification for
retrieval of fire catastrophes in databases of newscast
content. In the latter case, there are large variations in fire
and background characteristics depending on the video
instance. The proposed method analyzes the frame-to-frame
changes of specific low-level features describing potential
fire regions. These features are color, area size, surface
coarseness, boundary roughness, and skewness within
estimated fire regions. Because of flickering and random
characteristics of fire, these features are powerful
discriminant. The behavioral change of each one of these
features is evaluated, and the results are then combined
according to the Bayes classy- fire for robust fire
recognition. In addition, a priori knowledge of fire events
captured in videos is used to significantly improve the
classification results. For edited newscast videos, the fire
region is usually located in the center of the frames. This fact
is used to model the probability of occurrence of fire as a
function of the position. Experiments illustrated the
applicability of the method. Visual-based Smoke Detection
using Support Vector Machine Jing Yang, Feng Chen,
Weidong Zhang: Smoke detection becomes more and more
appealing because of its important application in fire
Hybrid Model Based on Color, Shape and Motion Detection for Video Surveillance by Raspberry-Pi
International Journal of Advanced Technology and Innovative Research
Volume. 09, IssueNo.02, February-2017, Pages: 0260-0267
protection. In this paper, we suggest some more universal
features, such as the changing unevenness of density
distribution and the changing irregularities of the contour of
smoke. In order to integrate these features reasonably and
gain a low generalization error rate, we propose a support
vector machine based smoke detector.
The feature set and the classifier can be used in various
smoke cases contrary to the limited applications of other
methods. Experimental results on different styles of smoke
in different scenes show that the algorithm is reliable and
effective. Face Image Abstraction by Ford-Fulkerson
Algorithm and Invariant Feature Descriptor for Human
Identification DakshinaRanjanKiskuDebanjanChatterjee, S.
Trivedy Massimo Tistarelli: This paper discusses a face
image abstraction method by using SIFT features and Ford-
Fulkerson algorithm. Ford-Fulkerson algorithm is used to
compute the maximum flow in a flow network drawn on
SIFT features extracted from a face image. The idea is to
obtain an augmenting path which is a path from the source
vertex to destination vertex with the available capacities on
all edges along a set of paths and flow is calculated along
one of these paths. The process is repeated until it is obtained
more paths with the available capacities. At the initial stage,
face image is characterized by SIFT (Scale Invariant Feature
Transform) features and the keypoints descriptor information
is taken as features set for further processing. Keypoint’s
descriptor is used to generate several face representations by
using a series of matrix operations which are further used to
determine a Directed Acyclic Graph (DAG). The resultant
directed graph contains sparse and distinctive face
characteristics of a subject from which the face image is
captured. We then apply the Ford-Fulkerson algorithm on
the directed graph to maintain the capacity constraints, skew
symmetry and flow conservation to obtain an augmenting
path with available capacities (relation between SIFT
points).
Finally, we obtain a mathematical representation of a face
image and this representation is further encoded to be used
as a set of distinctive features for matching. The time
complexity of the proposed face abstraction algorithm is
found to be O(VE2 ) where V is the set of vertices and E is
the set of edges in a directed graph. Optical Flow Estimation
for Flame Detection in Videos Martin Mueller, Member,
IEEE, Peter Karasev, Member, IEEE, Ivan Kolesov,
Member, IEEE, and Allen Tannenbaum, Fellow, IEEE:
Computational vision-based flame detection has drawn
significant attention in the past decade with camera
surveillance systems becoming ubiquitous. Whereas many
discriminating features, such as color, shape, texture, etc.,
have been employed in the literature, this paper proposes a
set of motion features based on motion estimators. The key
idea consists of exploiting the difference between the
turbulent, fast, fire motion, and the structured, rigid motion
of other objects. Since classical optical flow methods do not
model the characteristics of fire motion (e.g., non-
smoothness of motion, non-constancy of intensity), two
optical flow methods are specifically designed for the fire
detection task: optimal mass transport models fire with
dynamic texture, while a data-driven optical flow scheme
models saturated flames. Then, characteristic features related
to the flow magnitudes and directions are computed from the
flow fields to discriminate between fire and non-fire motion.
The proposed features are tested on a large video database
to demonstrate their practical usefulness. Moreover, a novel
evaluation method is proposed by fire simulations that allow
for a controlled environment to analyze parameter
influences, such as flame saturation, spatial resolution, frame
rate, and random noise. Detection of Multiple Dynamic
Textures Using Feature Space Mapping AshfaqurRahman
and ManzurMurshed, Member, IEEE: Image sequences of
smoke, fire, etc. are known as dynamic textures. Research is
mostly limited to characterization of single dynamic textures.
In this paper we address the problem of detecting the
presence of multiple dynamic textures in an image sequence
by establishing a correspondence between the feature space
of dynamic textures and that of their mixture in an image
sequence. Accuracy of our proposed technique is both
analytically and empirically established with detection
experiments yielding 92.5% average accuracy on a diverse
set of dynamic texture mixtures in synthetically generated as
well as real-world image sequences. Detection of Anomalous
Events in Shipboard Video using Moving Object
Segmentation and Tracking: Ben Wenger and
ShreekanthMandayam Patrick J. Violante and Kimberly J.
Drake Anomalous indications in monitoring equipment
onboard U.S. Navy vessels must be handled in a timely
manner to prevent catastrophic system failure. The
development of sensor data analysis techniques to assist a
ship's crew in monitoring machinery and summon required
ship-to-shore assistance is of considerable benefit to the
Navy.
In addition, the Navy has a large interest in the
development of distance support technology in its ongoing
efforts to reduce manning on ships. In this paper, we present
algorithms for the detection of anomalous events that can be
identified from the analysis of monochromatic stationary
ship surveillance video streams. The specific anomalies that
we have focused on are the presence and growth of smoke
and fire events inside the frames of the video stream. The
algorithm consists of the following steps. First, a foreground
segmentation algorithm based on adaptive Gaussian mixture
models is employed to detect the presence of motion in a
scene. The algorithm is adapted to emphasize gray-level
characteristics related to smoke and fire events in the frame.
Next, shape discriminant features in the foreground are
enhanced using morphological operations. Following this
step, the anomalous indication is tracked between frames
using Kalman filtering. Finally, gray level shape and motion
features corresponding to the anomaly are subjected to
principal component analysis and classified using a
multilayer Perceptron neural network. The algorithm is
exercised on 68 video streams that include the presence of
anomalous events (such as fire and smoke) and
benign/nuisance events (such as humans walking the field of
AKULA PRASHANTH, K. RAMBABU
International Journal of Advanced Technology and Innovative Research
Volume. 09, IssueNo.02, February-2017, Pages: 0260-0267
view). Initial results show that the algorithm is successful in
detecting anomalies in video streams, and is suitable for
application in shipboard environments. One of the principal
advantages of this technique is that the method can be
applied to monitor legacy shipboard systems and
environments where highquality, color video may not be
available.
IV. EXPERIMENTAL RESULTS
Most of the methods in the literature (especially the ones
based on the color evaluation) are tested using still images
instead of videos. Furthermore, no standard datasets for
benchmarking purposes have been made available up to
now. One of the biggest collections of videos for fire and
smoke detection has been made available by the research
group of Cetin. Starting from this collection, composed by
approx imative 31250 frames, we added several long videos
acquired in both indoor and outdoor situations so resulting in
a new dataset composed by 62.690 frames and more than
one hour of recording. More information about the different
videos is reported in Table I, while some visual examples are
shown in Fig.1. Note that the dataset can be seen as
composed by two main parts: the first 14 videos
characterized by the presence of fire and the last 17 videos
which do not contain fires; in particular, this second part is
characterized by objects or situations which can be wrongly
classified as containing fire: a scene containing red objects
may be misclassified by color based approaches, while a
mountain with smoke, fog or clouds may be misclassified by
motion based approaches. Such composition allows us to
stress the system and to test it in several conditions which
may happen in real environments. The dataset has been
partitioned into two parts: 80% has been used to test the
proposed approach while 20% for training the system by
determining the weights of the MES.
An overview of the performance achieved on the test set,
both in terms of accuracy and false positives, is summarized
in Table II. Among the three experts considered in this paper
(CE, ME and SV), the best one is the CE, which achieves on
the considered dataset a very promising performance
(accuracy = 83.87% and false positives = 29.41%). Note that
such performance is comparable with the one reached by the
authors in [8], where over a different dataset the number of
false positives is about 31%. On the other hand, we can also
note that the expert ME, introduced for the first time in this
paper for identifying the disordered movement of fire,
reveals to be very effective. In fact, we obtain 71.43%
accuracy and 53.33% false positives. It is worth pointing out
that the considered dataset is very challenging for this
expert: in fact, the disordered movement of smoke as well as
of trees moving in the forests can be easily confused with the
disordered movement of the fire. This consideration explains
the high number of false positives introduced by using only
ME. As expected, the best results are achieved by the
proposed MES, which outperforms all the other methods,
both in terms of accuracy (93.55%) and false positives
(11.76%). The very low false positive rate, if compared with
state of the art methods, is mainly due to the fact that ME
and SV act, in a sense, as a filter with respect to CE.
In other words, ME and SV are able to reduce the
number of false positives introduced by CE without paying
in terms of accuracy: this consideration is confirmed by the
results shown in Fig.3, where the percentage of the number
of experts which simultaneously take the correct decision is
reported. In particular, Fig.3a details the percentage of the
number of experts correctly assigning the class fire: we can
note that all the experts correctly recognize the fire in most
of the situations (69%), while two experts assign the class
fire in the remaining 31%. The advantage in using a MES is
much more evident inFig.3b, which refers to non fire videos.
In this case, only17% of videos are correctly classified by all
the experts. On the other hand, most of the videos (61%) are
assigned to the correct class by two experts, so confirming
the successful combination obtained thanks to the proposed
approach. In order to better appreciate the behavior
described above, a few examples are shown in Fig.2; in
Fig.2a the fire is correctly recognized by all the experts: the
color respects
TABLE I: The Dataset Used For The Experimentation
All the rules, the shape variation in consecutive frames is
consistent and the movement of the corner points detected is
very disordered. A different situation happens in
Fig.2b,where the only classifier detecting the fire is the one
Hybrid Model Based on Color, Shape and Motion Detection for Video Surveillance by Raspberry-Pi
International Journal of Advanced Technology and Innovative Research
Volume. 09, IssueNo.02, February-2017, Pages: 0260-0267
based on the color: in this case, the uniform movement of the
salient points associated to the ball as well as its constant
shape allows the MES to avoid a false positive introduced by
the use of a single expert. In Fig.2c and 2d other two
examples are shown: in particular, in the former a small fire
with a variable shape has both a uniform color and a uniform
movement of the Salient points.
Fig.1. Examples of images extracted from the videos used
for testing the method.
The combination of color and shape variation experts
helps the proposed system to correctly detect the fire. The
last example shows a very big but settled fire, whose Shape
is stable and so it is not useful for the detection. In
thissituation, the combination of the experts based on color
and motion allows the MES to take the correct decision
about the presence of fire.
Fig.2. The three experts in action; the red box indicates
the position of the fire, while the letter on it refers to the
expert recognizing the presence of the fire.
Fig.3. Number of experts simultaneously taking the
correct decision in fire (a) and non fire (b) videos. For
instance, 31% of situations are correctly assigned to the
class fire by two experts over three while in the
remaining 69% all the three experts correctly recognize
the fire.
Table II also shows a comparison of the proposed approach
with three recent, state-of-the art methodologies that have
been chosen because they too are based on the combined use
of color, motion and shape information. For we have used
two different versions: the origin alone, which, as proposed
by its authors, analyzes the images in the RGB color space,
and a version modified by us, working instead in YUV
AKULA PRASHANTH, K. RAMBABU
International Journal of Advanced Technology and Innovative Research
Volume. 09, IssueNo.02, February-2017, Pages: 0260-0267
space; we have chosen this modification on the base of [8],
where it is shown experimentally that color-based methods
work better in YUV than in RGB, as confirmed by the
results in Table II. The table shows that methods based on
the combination of different kinds of information
significantly outperform the single experts in terms of False
Positives; the difference in terms of False Negatives is not so
strong. Thus the combination helps more to improve the
specificity than the sensitivity of the system. The proposed
approach overcomes all the other considered methodologies
in terms of accuracy (93.55% against 89.29%, 90.32% and
87.10%,respectively). On the other hand, the best method in
terms of False Positives is (11.76% of the proposed approach
with respect to 5.88%).
TABLE II: Comparison Of The Proposed Approach
With State Of The Art Methodologies In Terms Of
Accuracy, False Positives And False Negatives
The better False Positive Rate is however balanced by an
improved False Negative Rate of our method, which shows
no False Negatives (i.e.no fires are missed) versus a 14.29%
False Negative Rate. While the difference between the two
algorithms in terms of accuracy may seem not very large, the
differences in the distribution of False Positives and False
Negatives can make each of the two methods preferable
depending on the requirements of the specific application. A
more detailed comparison for each of the considered videos
is shown in Table I of the Electronic Annex: we can note
that, differently from the other considered approaches, our
method achieves a 100% true positive rate, since it is able to
also retrieve very small flames (as the ones in videos
fire1,fire2, fire6 or fire13). This is mainly due to the
introduction of the MES for taking the final decision about
the event, which is able to detect the onset of small fires at
an early stage, when the amount of motion is still not very
large. It is also evident that the method is impressive for its
reduced false positive rate, causing on the whole dataset just
a single False Positive.
In order to further confirm the effectiveness of the
proposed approach, we also evaluated it over a second freely
available dataset (hereinafter D2)2. It is composed by 149
videos, each lasting approximative 15 minutes, so resulting
in more than35 hours of recording; D2 contains very
challenging situations, often recovered as fire by traditional
color based approaches: red houses in a wide valley (see
Fig.4a and 4d), a mountain at sunset (see Fig.4b) and lens
flares (bright spots due to reflections of the sunlight on lens
surfaces, see Fig.4a and 4c).Although the situations are very
challenging, no false positives are detected by our MES. The
result is very encouraging, especially if compared with CE,
achieving on the same dataset12% of false positives. It is
worth pointing out that such errors are localized in
approximative 7 hours, mainly at sunset, and are due to lens
flares. Such typology of errors is completely solved by the
proposed approach, able to take advantage of the disordered
movement of the flames. Finally, we have also evaluated the
computational cost of the proposed approach over two very
different platforms: the former is a traditional low-cost
computer, equipped with an Intel dual core T7300 processor
and with a RAM of 4GB.The latter is a Raspberry Pi B, a
Broadcom BCM2835 System-on-a-chip (SoC), equipped
with an ARM processor running at700 MHz and with a
RAM of 512 Mb. The main advantage in using such device
lies in its affordable cost, around 35 dollars.
Fig.4. Some examples of the Dataset D2, showing red
houses in the wide valley, the mountain at sunset and
some lens flares.
The proposed method is able to work, considering
1CIFvideos, with an average frame rate of 60 fps and 3 fps
respectively over the above mentioned platforms. Note that
60fps is significantly higher than the traditional 25 - 30 fps
that a traditional camera can reach during the acquisition. It
implies that the proposed approach can be easily and very
effectively used on existing intelligent video surveillance
systems without requiring additional costs for the hardware
needed for the images processing. In order to better
characterize the performance of the proposed approach, we
also evaluated the time required by the different modules,
namely the three experts (CE, ME and SV) and the module
in charge of updating the background, extracting the
Hybrid Model Based on Color, Shape and Motion Detection for Video Surveillance by Raspberry-Pi
International Journal of Advanced Technology and Innovative Research
Volume. 09, IssueNo.02, February-2017, Pages: 0260-0267
foreground mask and labeling the connected components
(FM). The contribution of each module is highlighted in
Fig.5 the average time required to process the single frame
has been computed and the percentage of each module with
respect to the total time is reported. We can note that SV
only marginally impacts on the execution time; this is due to
the fact that the search of the minimum bounding boxes
enclosing the blobs and of its properties (in terms of
perimeter and area) is a very low-cost operation. Although
the introduction of SV only slightly increases the
performance of the MES (from 92.86% to 93.55% in terms
of accuracy), the small additional effort strongly justifies its
introduction in the proposed MES.
On the other side, the higher impacts are due to ME and
CE: as for the former (85%), it is evident that the
computation of the salient points, as well as their matching,
is a very onerous operation. As for the latter, it may appear
surprising the big effort required by the CE with respect to
FM (CE: 11%, FM:2%). It is worth pointing out that FM’s
operations (such as background updating and connected
component labeling) are very common in computer vision,
and thus very optimized versions have been proposed in
standard libraries such as Open CV. Finally, it is worth
pointing out that the computation time is strongly dependent
on the particular image the algorithm is processing. In fact, it
is evident that pixel-based modules (such as FM and CE)
need to process the whole image independently of the
objects moving inside. On the other hand, it is evident that
the more are the objects moving inside the scene, the higher
is the effort required by FM for detecting and analyzing the
salient points. It implies that the variance with respect to the
overall time required for the computation is about 51%of the
overall time. Note that the final combination of the decisions
taken by the three experts has not been considered, since the
time required is very small with respect to the other modules.
In conclusion, the obtained results, both from a quantitative
and a computational point of views, are very encouraging
since they allow the proposed approach to be profitably used
in real environments.
Fig.5. The average execution time of our algorithm, in
terms of percentage of the total time for any expert (CE,
SVe ME) and the preliminary low level vision
elaborations (FM).
V. RESULT
Results of this paper is as shown in bellow Figs.6 to 10.
Fig.6. System activated message displaying “welcome” in
robot section.
Fig.7. System activated message displaying “welcome”.
Fig.8. Fire sensor activated.
Fig.9. camera checks the height and motion of the fire.
AKULA PRASHANTH, K. RAMBABU
International Journal of Advanced Technology and Innovative Research
Volume. 09, IssueNo.02, February-2017, Pages: 0260-0267
Fig.10. GSM it sends the message to monitoring section
and the buzzer will be on.
VI. CONCLUSION
In this paper we propose a fire detection system using an
ensemble of experts based on information about color, shape
and flame movements. The approach has been tested on a
wide database with the aim of assessing its performance both
in terms of sensitivity and specificity. Experimentation
confirmed the effectiveness of the MES approach, which
allows to achieve better performance in terms of true
positive rate with respect to any of its composing experts.
For the future work will be devoted to the integration in the
same MES framework of a smoke detection algorithm, and
to the extension of the approach to operating conditions
currently not covered, such as its execution directly on board
of the camera, and its use on Pan-Tilt-Zoom cameras.
VII. REFERENCES
[1] Pasquale Foggia, AlessiaSaggese and Mario Vento,
IAPR Fellow, “Real-time Fire Detection for Video
SurveillanceApplications using a Combination of Experts
based on Color, Shape and Motion”, IEEE Transactions on
Circuits and Systems for Video Technology.
[2]A.E.Cetin,K.Dimitropoulos,B.Gouverneur,N.Grammalidi
s, O. Gunay,Y. H. Habiboglu, B. U. Toreyin, and S.
Verstockt, “Video firedetection: a review,” Digital Signal
Processing, vol. 23, no. 6, pp. 1827– 1843, 2013.
[3] Z. Xiong, R. Caballero, H. Wang, A. Finn, and P.-
y.Peng, “Video firedetection: Techniques and applications in
the fire industry,” in MultimediaContent Analysis, ser.
Signals and Communication Technology,A. Divakaran, Ed.
Springer US, 2009, pp. 1–13.
[4]T. Celik, H. Demirel, H. Ozkaramanli, and M. Uyguroglu,
“Fire detectionusing statistical color model in video
sequences,” J. Vis. Comun.Image Represent., vol. 18, no. 2,
pp. 176–185, Apr. 2007.
[5] H.-Y. J. Yoon-Ho Kim, Alla Kim, “Rgb color model
based the firedetection algorithm in video sequences on
wireless sensor network,”International Journal of Distributed
Sensor Networks, 2014.
[6] C. Yu, Z. Mei, and X. Zhang, “A real-time video fire
flame andsmoke detection algorithm,” Procedia Engineering,
vol.62,no.0, pp.891–898,2013, 9th Asia-Oceania Symposium
on Fire Science andTechnology.
[6] X. Qi and J. Ebert, “A computer vision-based method for
fire detectionin color videos,” International Journal of
Imaging, vol. 2, no. 9 S, pp.22–34, 2009.
[8] T. Celik and H. Demirel, “Fire detection in video
sequences using ageneric color model,” Fire Safety Journal,
vol. 44, no. 2, pp. 147–158,2009.
[9] T. Celik, H. Ozkaramanli, and H. Demirel, “Fire pixel
classificationusing fuzzy logic and statistical color model,”
in ICASSP, vol. 1, April2007, pp. I–1205–I–1208.
[10] B. C. Ko, K.-H. Cheong, and J.-Y. Nam, “Fire detection
based on visionsensor and support vector machines,” Fire
Safety Journal, vol. 44, no. 3,pp. 322 – 329, 2009.
[11] M. Mueller, P. Karasev, I. Kolesov, and A.
Tannenbaum, “Optical flowestimation for flame detection in
videos,” IEEE Trans. Image Process,vol. 22, no. 7, pp.
2786–2797, July 2013.
[12] A. Rahman and M. Murshed, “Detection of multiple
dynamic texturesusing feature space mapping,” IEEE Trans.
Circuits Syst. Video Technol.,vol. 19, no. 5, pp. 766–771,
May 2009.
Author’s Profile:
Mr.Akula Prashanth has completed his
B.tech in ECE Department from Netaji
institute of Engg. & Technology, JNTU
University, Hyderabad. Presently, he is
pursuing his Masters in Embedded system
from kasireddy Narayan Reddy college of
Engineering and Research, Hyderabad, TS.
India.
Mr.K.Rambabu has completed B.Tech
(ECE) from Sri Kottam Tulsi Reddy
Memorial College of Engineering,
Mahaboobnagar Dist.,JNTUH University,
M.Tech (Image Processing) from Aurora
College of Engineering, JNTUH
University, Hyderabad. He is having 6
years of experience in teaching field. Currently, He is
working as an Associate Professor and HOD of ECE
Department in kasireddy Narayan Reddy college of
Engineering and Research, Hyderabad, TS. India.