Modeling of Hybrid Surveillance Robot for Detection of Conflagration

Transcription

1 Modeling of Hybrid Surveillance Robot for Detection of Conflagration L.M.I. Leo Joseph, S.R Engineering College, Ananthasagar (Village), Hasanparthy, Warangal, India B. Girirajan S.R Engineering College, Ananthasagar (Village), Hasanparthy, Warangal, India ABSTRACT The word conflagration also referred as fire detection plays a vital role in protecting the human society and natural resources. This approach reaches out the bottlenecks in nurturing the denominations that are root caused either by artifacts or natural occurrence. It has fascinated a great deal of attention in view of safety and protection aspects. This paper highlights a fire detection system via Image Processing, which gives a confined solution by means of image acquisition orientated fire detection. For this, we propose a smart robot inbuilt with surveillance camera and GPS. The acquired image and positional data are manipulated by audrino processor and are transmitted to the control room. The proposed fire detection system is confined with light and image pattern detection which in turn intimate the exact footprint of the disaster. This approach incorporates image acquistion,geo positioning with the aid of ambient light. Fire growth is analyzed and calculated by correlating patterns in the acquired frames. Based on the fire growth the robot move near to the location the movement of robot can be tracked by GPS and the footprints of fire can be visualized instantly by surveillance camera. Keywords conflagration detection,hybrid robot, GPS, imageacquisition, audrino processor. 1.INTRODUCTION Image processing is the art of defining the picture as per our need which helps to get better quality of the picture. Image processing precincts with restoration, segmentation, denoising etc...the image processing now find its vicinity in detecting conflagration which is a high threatening and has unfathomable solution even though we are having very sophisticated technologies with us. Researches and case studies has been enforced to curtail the deflagration in its premature stage itself[5]. The researchers till working on to find the complete solution for this disaster. Example to show our inefficiency is the burning Jharia coal mines which not only affects the society but also results in the heavy loss in the lack of retention of natural resources. In this aspect we work out a proposal to find a suitable solution to identify the conflagration and to curtail its roots in its premature stage itself.. Many traditional methods are available which involves basic sensors in conflagration detection. These traditional methods couldn't solve the issue due to the less sensitivity nature of the sensors. The sensing element needs lot of computational procedures as it depends on many parameters [2]. Sometimes wrong prediction may also possible while incorporating the conventional sensors. In our propose method we are using a image capturing technique by using a camera molded to movable robot.this robot are used as surveillance robot to detect conflagration. When a conflagration occurs inside the geo fence of robot surveillance area[7], the robot move nearer to the conflagration point and capture the image without crossing the geo fence limit. The geo fence limit is decided depending on the area of usage[19]. If the growth rate present in the frame seems growing beyond the threshold then the GPS sensor fitted with the robot calculates the latitude and longitude information of the area under surveillance. These information are decoded and processed by a VLSI processor and are send to fire brigades for necessary action. The paper presented was organized as follows. The reconfigurable architecture has been are discussed in section II. The details of 309 L.M.I. Leo Joseph, B. Girirajan

2 GPS sensor, VLSI processor and data manipulation techniques are discussed in section III. Experimental results are provided in section IV. The final remarks and future works are discussed in section V. 2. RECONFIGURABLE COMPUTING Fig1. Reconfigurable computing Architecture In this article partial re-configuration architecture is identified such that to confine the process as per our need.partial Reconfiguration is the process of redefining a part of the hardware circuit while the function of supplementary part of the circuitry remains impassive..fpga are widely acknowledged to support partial reconfiguration. Normally, reconfiguring an FPGA is reloading a intend on a FPGA by making the FPGA to reset.. Partial reconfiguration allows loading FPGA with a limited intend into a reconfigurable unit. Partial reconfiguration stores only the design required by the process which results in saving of space. Partial reconfiguration seems sometimes incompatible to some FPGAs. A flow of special software emphasized with modular design is essential. The design modules built inside the FPGA required special mapping in order to fit the mapping protocols of internal hardware[19]. 3.PROPOSED SYSTEM The proposed system employs real time processing technology in which VLSI processor (Spartan3E) is used for image acquisition and manipulation. In this proposed method, camera is used as a image acquisition device. Data manipulation, Transmission and control are governed by VLSI processor. The VLSI processor is programmed using 6 LUT's (Look Up Table) that are used in analysis and control of different parameters [8]. The functions of LUTs are briefly discuss in the following phases. FRAME ANALYZER MOTOR ACTIVATOR GEO FENCING VERIFIER GROWTH RATE COMAPARATOR GPS TRANSMITTER VLSI PROCESSOR Figure 2: Proposed System 310 L.M.I. Leo Joseph, B. Girirajan

3 3.1 FRAME ANALYSER Frame analyzing is a surveillance technique which uses a camera for monitoring the environmental changes in a 24 x 7 basis. The information recorded each and every second is continuously compared with the standard frames which are already stored as a reference in the centralized server[3]. The LUT 1 is programmed to analyze the acquired data with the standard data's. 3.2 MOTOR ACTIVATOR In this proposed we incorporate a movable image acquisition process which will be activated and controlled by a LUT 2. For Instance if there is any anonymous occurrence exists in the frame then the LUT 2 triggers the motor that helps to drive the robot to the point where anonymous gestures are found. This movable technique find its effectiveness in identifying the conflagration with accurate footages whereas in earlier methods the image acquired is just a growth rate in which point of accuracy seems very less [7] than that of the projected method. 3.3 GEO FENCING VERIFIER In order to ensure the device safety, the LUT 3 is programmed with geo fencing technique which is used to protect the robot from entering into the area of concentration. This device is modeled to acquire image from the radius of 5 m from the area of concentration. The radius of curvature is calculated from the point of concentration, so that if the frame analyzer identifies the point the robot moves near the point and stops before 5m from the point. The distance is estimated by ordinary radius of curvature calculation of circle since geo fencing is a circle of interest from which image acquisition takes place. 3.4 GROWTH RATE COMPARATOR A burning fire may extends either upside or downside which depends on the air flow and fuel type. when a conflagration is detected its boundaries are recorded for comparison. The growth rate information is extracted from the next n frames. LT (Left), RT (Right), UD(Upward), DD(Downward) direction growth rate are measured distinctly. The growth rate are estimated in frame by frame manner in all the four directions. The summation of growth rate is estimated in which the points for the upward direction keeps on increasing than that of left side, right side and downside [9]then the detected frame growth is suspected to be a conflagration. The estimated points are stored as a standard threshold for further detection process. The acquired image contains any feature of conflagration then the acquired image is compared with the standard data base stored in the central server as explained[7]. Initially the processor is tuned with some predefined threshold that is used to recognize the seriousness of the conflagration. If the acquired image threshold seems lesser than that of the standard threshold then the robot just fix its position there and keeps on monitoring until new point of focus occurs[10]. In other hand, if the acquired image is greater than that of the standard threshold than the VLSI processor activates the GPS for calculating latitude and longitude details of the area of surveillance. Fig 3: Growth rate comparison that seems higher in upward direction 311 L.M.I. Leo Joseph, B. Girirajan

4 3.5 GLOBAL POSITIONING SENSOR [GPS] AND TRANSMITTER In this proposed system Gps are employed to estimate latitude and longitude details of the acquired area.in general the earth bio data can be analyzed with the help of GPS which provides LATLONG details accurately. These information's are further mapped on the suitable mapping applications in order to detect the point of concentration at the ease. The calculated details are send to the control room by means of a transmitter which is programmed to send information in decoded format. In this proposed technique data broadcast is very easy as we are sending the details in the form of encoded binary data's. This produce as a better point of accuracy when comparing with the visual analysis techniques. The developed technique can effectively detect the conflagration in real-time. The accuracy of the above technique is very high. In this proposed technique the occurrence of fault detection due to lighted candles, lighted matches are eliminated due to fixation of threshold based on frame rate. The proposed technique has been further improved by incorporating automated fire control techniques before manual force come into effect. Figure 4. Estimation of Latlong details of area of concentration Figure 4: Implementation of GPS module at the receiver end 4. EXPERIMENTAL RESULTS The results are simulated using MATLAB software and VLSI simulation tools. Figure 6 and Figure 7 shows various images for which this method has been applied. When the code is run, based on the growth comparator estimation, Latitude and longitude are calculated and encoded into binary data's as shown. Figure 6: Images Captured that has more threshold values 312 L.M.I. Leo Joseph, B. Girirajan

5 Figure 7: Transmitter output with decoded Latitude and Longitude positions 5.CONCLUSION Detection of deflagration based on image processing is a good approach to detect the fire early and to stop huge loss towards life and property. The proposed fire detection system is an accurate method which can be used in vast areas without any constraints.. There is a good scope for further improvements like the inclusion of adaptive fire control system. This method also includes geo fence provisions which become effective in protecting the system from dangerous hazards. This method provides a complete information for the user end in accurate detection of conflagration without going to any probability analysis. The main draw back in this system is GPS failure and poor sensitivity of GPS sensor may lead to minimize the expectation of device performance. REFERENCES [1] Jareerat Seebamrungsat, Suphachai Praising and Panomkhawn Riyamongkol, "Fire Detection in the Buildings Using Image Processing", The 2014 Third ICT International Student Project Conference, March 2014, pp [2] Supriya Bhargava, Anand Vardhan Bhalla Performance Improvement of Vision Based Fire Detection System, International Journal of Emerging Technologies in Engineering Research (IJETER) Volume 1, Issue 1, July 2015, pp [3] Turgay Celik, Fast and Efficient Method for Fire Detection Using Image Processing, ETRI Journal, Volume 32, December 2010, pp [4] Jianhui Zhao, Zhong Zhang, Shizhong Han, Chengzhang Qu, Zhiyong Yuan, and Dengyi Zhang, "SVM Based Forest Fire Detection Using Static and Dynamic Features" ComSIS Vol. 8, No. 3, June 2011, pp [5] Turgay Çelik, Hüseyin Özkaramanlı, and Hasan Demirel, Fire and Smoke detection without sensors: Image Processing based approach, 15th European Signal Processing Conference, September 3-7, 2007, pp [6] Neelamma K. Patil, Ravi M. Yadahalli, Jagadeesh Pujari, Comparison between HSV and YCbCr Color Model Color-Texture based Classification of the Food Grains, International Journal of Computer Applications ( ) Volume 34 No.4, November 2011, pp L.M.I. Leo Joseph, B. Girirajan

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