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Abstract— Vision based fire surveillance is a useful technique.
With the increased number of surveillance cameras, a vision
based fire detection capability can be incorporated in existing
surveillance systems at relatively low cost. Vision based fire
detection offers advantages over the traditional methods. In this
paper we provide an ideal solution for surveillance that involves
the assortment of necessary sensors and devices using pertinent
tools.In our project, we are aimed at developing a low- cost, real-
time video surveillance kit which will be proficient of providing
real-time video streaming of the area which we want to observe.
During rescue mission it can lessen the human effort in
menacingcircumstances. A Bluetooth module is used for the
communication between the controller and the android
application. We will program the microcontroller in such a way
that say whenever the smoke sensor perceives smoke in air and if
the temperature is beyond a certain threshold level, temperature
status, smoke level etc. will be immediately sent and stored into
mobile via Bluetooth. Thereby we can detect the safest path thus
avoiding loss of life.
Index Terms—Live Video streaming, (HC-05)Bluetooth
module, Arduino,Android Mobile application.
I. INTRODUCTION
According to the studies conducted, it is observed that inthe
event of any natural calamitythe first 48 hours are verycritical
in saving human life.Fire rescue is one of the most important
public safety activities.Development of automated and smart
video surveillance systems has been the core trend. This
project presents a simple and effectual method for detecting
the fire crisis automatically in the monitoring area via live
video transmission.By observing and utilizing features of fire
event, a quick and accurate detection process is developed for
early fire warning purpose thus to reduce the mortality rate
and loss of property caused by fire accidents. The proposed
algorithm not only achieves real-time requirements and has
better performance (more robust and correct) than the existing
surveillance systems, but also cost effective. Our fire detection
system is a reliable system for surveillance and recognition of
spontaneous fires inside commercial buildings or homes.Real
time applications, such as habitat surveillance, environmental
and structural observance,start to work in practical since they
have attracted a lot of attention in recent years. The proposed
system works in case if predefined temperature limits are
exceeded and the smoke is detected in the smoke
sensor.Alarms and the system status are displayed on the
monitor of the android device. By observing that the demand
of using the digital surveillance system gets increasing, an
intelligent, integrated, and automatic system should be
developed to overcome the drawback in the existing systems.
People and building are getting more crowded in urban area,
thus it makes fire accident more harmful than sparse area. The
main objective of this work is to develop a fully automatic
warning and surveillance system for potential danger in the
monitoring area via video transmission.In this paper, the
sections to be discussed are arranged sequentially. Some
similar and existing technology discussed in some paper are
reviewed in Section II. The proposed system architecture &
features are discussed in Section III. The system
implementation against the features are discussed in Section
IV. Finally the future scope& conclusion is remarked in
Section V.
II. RELATED WORKS
There are large number of papers related to fire detection. One
such paper is about fire detection in computer vision literature.
A probabilistic approach is used for vision based fire
detection. It is one of the method for identifying fire in videos.
It has a background control for CCTV surveillance.
Additionally an automatic video classifier is used. The
features for detection include color, area, size, boundary
roughness and skewness that describe the fired region. It also
proposes the use of variance because of the randomness in fire
surfaces. Bayes classifier is used to achieve this practically.
All these features are considered as a major discriminants
because of the flickering and random characteristics of blaze.
A similar paper for detecting fire uses flame detectors. With
the advance in IT, optic temperature sensor is widely used to
detect fire. The cost is too high, the basic working is to judge
the color and motion of fire using video processing technique.
They cannot detect the temperature, gas and other sources of
fire. The video processing technique is based on thermal
image. For thermal image, they use omnidirectional thermal
sensors which uses eight thermal infrared sensors for 360
degree motion detection and tracking. The thermal camera
gives only the raw thermal data. The fire detection device
comprises of 3 important things-the first one is thermal
camera, the second one is conic mirror for omnidirectional
view and the last one is pan-tilt module for wide and narrow
field view. The mirror system consists of three mirrors. The
first one is conic mirror which is easy to build. The next is
spherical mirror which is also easy to build. The last one is
hyperboloid mirror which is difficult to machine. The pan-tilt
module provides two modes (i) Wide range search mode by
SURVEILLANCE AND APPREHENSION OF BLAZE WITH MOBILE
APPLICATION INTELLIGENCE
Mrs. K.Lavanya, Assistant Professor, A.Aishwarya, D.Kavya, M.Shwetha, S.Srinidhi
Velammal Engineering College.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 6, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
Page 282 of 285
positioning the thermal camera upward to conic camera. (ii)
Narrow range accurate search mode. Finally method detects
sources of fire detection only within 1.5m distance range. In
the prevailing methods of fire detecting systems an approach
is based on video stabilization which is completely reliable on
SURF which involves two main steps: interest point detection
and interest point description which are implemented by
systolic two layer array Architecture designed on an FPGA
platform. The other approach involves rescue missions of
victims finding, positioning and identification. While the state
of the victim is not completely achievable autonomously and it
needs a continuous monitoring end that is challenging which
ultimately consumes time that is unfit for a fire accident
situation. Also the use of audio sensor is not advisable as it
may encounter false-positive victim detection. With reference
to the related work, it builds an end to end connectivity over
wireless networks between two heterogeneous systems for live
video streaming. In this system, the wireless networks are
varying in time and have inferior performance when compared
to wired network. Moreover, it requires short transition time
during hand off and thus the video quality gets deteriorated.
III. DESIGN AND IMPLEMENTATION
A. ATMEGA 2560 ATMEGA 2560 has the following pins: 54input and output
pins,16 analog inputs,4 UART’s which are hardware serial
ports, ATMEGA 2560 is a arduino mega 2560 microcontroller
based board. 16 MHz crystal oscillator, ICSP (Incircuit serial
programming) header, power jack, USB connection, a reset
button. The arduino software (IDE) is used to program the
ATMEGA microcontroller. The Integrated Development
Environment(IDE) runs both online and offline on the boards
and allows to write and upload programs on board. The
microcontroller is coded using C language
B. Firesensor The fire sensor that we use is silicon PIN photodiode which is
BPV10. It is very fast and sensitive to blaze with a plastic
package. The device is sensitive to visible and infrared
radiation. It has many features like fast response time, high
sensitivity to flame, high bandwidth of 250 MHz at 12 V.
Application : Used as wide band detector for industrial
electronics, interrupters and control circuits.
C. Smoke sensor MQ-2 is a semiconductor sensor for detecting combustible
gases.MQ-2 gas sensor has a sensitive material of 𝑆𝑛𝑂2
which has lower conductivity in clean air. When the gas
concentration increases in the fired location, the
sensors’sconductivity also raises and this increase in
conductivity corresponds to the gas concentration output
signal. It has high sensitivity to propane, hydrogen, LPG,
methane and other combustible gas. The features are long life
with low cost and has a simple drive circuit.
Application: Used as portable gas detector, Industrial
combustible gas detector, domestic gas leakage detector.
D. Temperature sensor The temperature sensor is of two types: contact and non-
contact. The contact sensors measure the thermal radiation
from the object and then measure the temperature. The non-
contact sensors measure the temperature directly from a
distance in a hazardous environment. The temperature that is
read in the environment is the voltage that is read across the
diode.
E. Relay Relays are small electromagnetic switch that are operated by
small electric currents. It acts as both switches and amplifiers.
The relays are made of a coil which acts as a temporary
magnet when electricity flows through it. Generally, the
various sensors that we use are sensitive electronic equipment
that often produces only small current. This current is not
enough to drive big apparatus. So the relay helps small
currents to activate the larger objects by converting small
currents into larger currents thereby acting as amplifiers here.
They are generally single pin or dual pin, 3-pin, 4-pin, 5-pin,
6-pin, etc.
Applications: Used in telephone switching and as amplifiers
till the arrival of transistors.
F. Bluetooth HC-05 module is a bluetooth serial port protocol (SPP)
module which is used to communicate with two arduino
boards. The arduinos communicate via serial communication.
HC-06 can be only set as slave while HC-05 can be set as both
master and slave between two arduino boards. This module is
fully qualified bluetoothV2.0+EDR(Enhanced data rate) with
2.4 GHz radio baseband and transceiver. It uses CSR
bluecore-04 with CMOS technology and adaptive frequency
hopping(AFH). The slave mode is the default factory setting.
In slave mode it can accept only connections and cannot
initiate connections while in master mode it can initiate
connections. HC-05 has 6 pins.
Enable : When it is connected to 3.3V the device is on and
communication takes place.
𝑉𝑐𝑐 : 3.3V-5V.
Gnd: Ground pin.
TXD and RXD: UART interface for communication.
Status: Acts as status indicator.
Button: Switch the module into AT command mode.
G. Power supply The ac voltage which is 220 V rms is connected to
transformer which steps down the voltage to desired DC
output level. The diode rectifier provides full wave rectified
voltage that is initially filtered by a simple capacitor to
produce a DC voltage. The resulting DC voltage has some
ripples and the ripples are removed by the regulator circuit
andthe DC value remains the same even if the input DC
voltage varies or the load connected to the output DC voltage
changes. The voltage regulation is obtained by one of the
popular voltage regulator IC circuits.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 6, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
Page 283 of 285
IV. PROPOSED WORK
In the proposed system, Arduino Mega 2560 is used as the
Control unit through which various sensors are controlled.
The goal is to develop a real time video surveillance.
Arduino kit which will be fixed at various locations in
commercial buildings. The kit developed is interfaced with
temperature sensor, flame sensor, smoke sensor, camera,
Bluetooth module and an LCD display. These are usually
cheap and stationary solution for fire detection. The sensors
are interfaced with the analog input pins of the control unit.
The data collected from the sensors are viewed through the
LCD display. The display information includes temperature
rise, presence of smoke and flame of the surrounding
environment. If the temperature rises above the threshold
value of 45 degree Celsius or the flame is detected through
the IR sensor, then an alert is given via the DF Mini player
to the people in the fired building. The same information is
obtained in the developed Android Application to the
security personnel who will be given access. The smart
microcontroller unit named as Arduino Mega 2560 can be
programmed with the Arduino IDE softwareto upload new
code to it without the use of an external hardware
programmer. The IP web camera is used to enhance the
situation awareness of the damaged environment through
live video streaming. The harmful and poisonous gas is
detected from the gas sensor so to protect the victims from
death. A HC-05 Bluetooth module is used for the
communication between the Arduino controller and the
android application.Communication is using the original
UART protocol (reference, C header files). Additionally, the
DF Player Mini SKU gives necessary commands about the
safest path to the victims so that the victims can escape via
non-fired regions. This protects them from various fire
disasters as well as from other destructions. Finally, the
whole system is designed to obtain the required quality
video streaming and the necessary parameters are viewed.
ALGORITHM
1. Start.
2. LCD displays “FIRE SAFETY SYSTEM” and
temperature of each floor.
3. Micro-controller reads the input from temperature
sensor, flame sensor and gas sensor.
4. If poisonous gas leaks or detects smoke or detects fire,
i. HC-05 Bluetooth module turns on.
I. Mobile application indicates the fire
prone floor with the corresponding
exit number.
ii. Speaker turns on.
iii. LCD displays the fired floor.
Else microcontroller continuously reads input from the
sensors.
5. Stop.
Fig., Block diagram
V. RESULTS AND DISCUSSION
The developed project was tested with inputs to different
sensors and was found to work accordingly. The response for
the input to the sensors were noticed on the LCD display,
mobile application and DLF Mini player. The LCD Display
displays the floor where fire has occcured and it is also
specified in the mobile application using a flame image. The
DF Mini player plays the voice recorded in the SD card
according to the current situation describing the fire prone and
the safe floors, by collecting information from the sensors.
The above figure shows the output for fire detected on the first
floor by the fire sensor indicating the first exit. The DF Mini
player plays the audio indicating that the first floor has caught
TRANSFORMER RECTIFIER FILTER
IC REGULATOR LOAD
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 6, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
Page 284 of 285
fire whereas second and third floors are safe.The second and
the third exits of each floor corresponds to the gas and
temperature sensors respectively. When any of thepoisonous
gases such as LPG, Butane, Propane, Methane, Alcohol,
Hydrogen and Combustible steam is detected, then it indicates
fire in the second exit of the corresponding floor in the mobile
application along with the DF Mini player alert. When the
room temperature exceeds the preset threshold temperature, it
indicates fire and flame symbol is indicated in the third exit of
the corresponding floor along with the DF Mini player alert.
VI CONCLUSION
Thus this project brings an intelligence to view and
communicate a situation during a blast using microcontrollers
and mobile application. Thus the camera linked with the
device provides a live video surfing of the blast environment
that ultimately guides the control unit.In addition to this, the
system is supported by an automated alarm triggering. The
success of any innovation relies on the attribute against real
life problems and its viability. Fire is one of the dangerous
events which can result in great losses if it is not controlled on
time. This necessitates the importance of developing early fire
detection systems. Therefore, in this research article, we
propose a cost-effective fire detection.Additional
improvement in the design and functionality of the kit could
make this project more successful.
VII FUTURE WORK
It is vital to note that noise generated by the sensors are an
important factor and cannot be ignored and has certain impact
on the control systems. At future works many filters, both
analog and digital can be used to filter sensor noise to provide
a better control. Complexity of the system components,
frustrated environmental factor and unpredictable human
factorsmay affect the performance of the system seriously.
This is verified with the help of fire proof Wi-Fi camera which
has 360 degree vision, full HD picture quality, full color in
low light, two way audio talk back features. The camera is
turned on by the controller as soon as fire is detected. The
application can be further evolved such that it automatically
notifies the nearby fire station and the entitled person
regarding the event of fire accident and update them the
ongoing situation.The obtained particulars regarding the zone
of fire can be employed for improvisation of the mobile
application, to acquire the safest path inside the building. This
could be utilized by the firemen in an effective manner for
quick and safe entry into the building been in flames. This
could potentially reduce the loss of lives and property.
REFERENCES
[1] Khan Muhammadi,Jamil Ahmadi, IrfanMehmood, SeungminRho3 and
Sung WookBaiki, “Convolutional Neural Networks based Fire Detection in
Surveillance Videos.,”IEEE Access,vol.6.06 March 2018, DOI:
10.1109/ACCESS.2018.28128355.
[2] Paulo ViniciusKoerich Borges and EbroulIzquierdo, “A Probabilistic
approach for vision based fire detection in videos,” IEEE Transactions on Circuits and Systems for video technology., vol. 13, no. 1, pp. 11-23, Jan.
1995, DOI: 10.1109/TCSVT.2010.2045813.
[3] TahiyahNouShene, K. Sridharan and N. Sudha, “Real-Time SURF-Based
Video Stabilization System for an FPGA-Driven Mobile Robot,” IEEE
Transactions on Industrial Electronics., vol. 11, no. 2, pp. 115–117, Feb.
1986, DOI: 10.1109/TIE.2016.2551684.
[4] Sangchun Han, Hyunchuljoo, Dongju Lee and Hwangjun Song,
“An End-to-End Virtual Path Construction System for Stable Live
Video Streaming Over Heterogeneous Wireless Networks.,” IEEE Journal On Selected Areas In Communications, vol. 29, no. 5, May
2011, DOI: 10.1109/JSAC.2011.110513.
[5] Pasquale Foggia, AlessiaSaggese and Mario Vento, “ Real-time
Fire Detection for Video Surveillance Applications using a
Combination of Experts based on Color, Shape and Motion.,” IEEE Transactions on Circuits and Systems for Video Technology,vol.
25 , no. 9 , Sept. 2015, DOI:10.1109/TCSVT.2015.2392531
[6] ByoungChulKo, Kwang-Ho Cheong, Jae-Yeal Nam, “Fire
detection based on vision sensor and support vector machines.,” Fire Safety J., vol. 44, no. 3, pp. 322–329,Apr. 2009.
[7] C. L. Lai, J. C. Yang, and Y. H. Chen, “A real time video
processing based surveillance system for early fire and flood
detection.,” in Proc. IEEE Instrum . Meas. Technol. Conf., Warsaw,
Poland, May 2007, pp. 1–6. [8] P. Barmpoutis, K. Dimitropoulos and N. Grammalidis, “Real time
video fire detection using spatio-temporal consistency energy., ” IEEE International Conference on Advanced Video and Signal-Based Surveillance, Krakow, Poland, 2013.
[9] Jung-Hoon Hwang, Sewoong Jun, Seung-Hun KimDonghoon Cha
and Kaehoon Jeon,”Novel fire detection device for robotic fire
fighting,” ICCAS 2010,30 Oct 2010,
DOI: 10.1109/ICCAS.2010.5669964.
[10] Y. H. Habiboglu, O. Günay, and A. E. Çetin, "Covariance matrix-
based fire and flame detection method in video.," Machine Vision and Applications, vol. 23, pp. 1103-1113, 2012.
[11] M. Mueller, P. Karasev, I. Kolesov, and A. Tannenbaum, "Optical
flow estimation for flame detection in videos.," IEEE Transactions on Image Processing, vol. 22, pp. 2786-2797, 2013.
[12] R. Di Lascio, A. Greco, A. Saggese, and M. Vento, "Improving
fire detection reliability by a combination of videoanalytics.," in International Conference Image Analysis and Recognition, 2014, pp.
477-484.
[13] A. Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S. W. Baik,
"Action Recognition in Video Sequences using Deep Bi-directional
LSTM with CNN Features.," IEEE Access, vol. PP, pp. 1-1, 2017.
[14] A. Ullah, J. Ahmad, Khan Muhammad, Irfan Mehmood, Mi
Young Lee, Jun Ryeol Park, Sung WookBaik, "Action Recognition in
Movie Scenes using Deep Features of Keyframes.," Journal of Korean Institute of Next Generation Computing, vol. 13, pp. 7-14, 2017.
[15] J. Yang, B. Jiang, B. Li, K. Tian, and Z. Lv, "A fast image
retrieval method designed for network big data.," IEEE Transactions on Industrial Informatics, 2017.
[16] D. Y. Chino, L. P. Avalhais, J. F. Rodrigues, and A. J. Traina,
"BoWFire: detection of fire in still images by integrating pixel color
and texture analysis.," 28th SIBGRAPI Conference on Graphics, Patterns and Images, 2015, pp. 95-102, 2015
[17] S. Verstockt, T. Beji, P. De Potter, S. Van Hoecke, B. Sette, B.
Merci, et al., "Video driven fire spread forecasting (f) using multi-
modal LWIR and visual flame and smoke data.," Pattern Recognition Letters, vol. 34, pp. 62-69, 2013.
[18] B. C. Ko, S. J. Ham, and J. Y. Nam, "Modeling and formalization
of fuzzy finite automata for detection of irregular fire flames.," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21,
pp. 1903-1912, 2011.
[19] S. Rudz, K. Chetehouna, A. Hafiane, H. Laurent, and O. Séro-
Guillaume, "Investigation of a novel image segmentation method
dedicated to forest fire applications.,”Measurement Science and Technology, vol. 24, p. 075403,June 2013, DOI:
10.1088/0957233/24/7/075403.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 6, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
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