Webcam Enabled Raspberry PI Fire Detection System

Transcription

1 Webcam Enabled Raspberry PI Fire Detection System K.Sharmila 1, Mr.M.Sudhakaran 2, Mr.S.Iyyappan 3, Dr.R.Seyezhai 4 1 PG Scholar, Department of EEE, Ganadipathy Tulsi s Jain Engineering College, Vellore, India. 2 Associate Professor, Department of EEE, Ganadipathy Tulsi s Jain Engineering College, Vellore, India. 3 Assistant Professor, Department of EEE, Ganadipathy Tulsi s Jain Engineering College, Vellore, India. 4 Associate Professor, Department of EEE, SSN College of Engineering, Chennai, India. ABSTRACT Fire is a huge disaster which leads economic and environmental losses. So it is necessary to detect occurrence of fire at earlier stages. This paper describes a model to detect fire by analyzing the videos captured from webcam which alerts us through Wi-Fi. This system implementation helps us to track each and every instant of fire in our surroundings. Here Open CV library is used to process video more accurately with less time period. The proposed Fire detection algorithm overcome drawbacks of sensor based fire detection methods such as false alarm and larger response time. The main consideration of this system is based on two algorithms mainly used for flame detection. They are motion and color which can be achieved by color segmentation process followed by edge detection. This project deals with the latest technology called the Raspberry Pi based control system for implementing fire detection model. Keywords: Color detection, Edge detection, Raspberry-pi, Opencv-python 1. Introduction Fire accidents cause economical loses and hurts all living beings. It is more prudent to head off a disaster beforehand than to deal with it after it occurs. In order to announce about fire occurrence many methods are followed. Fire alarms are one of the most useful ways to avoid fire deaths. A fire alarm system is part of the total security system providing fire protection. Installation of fire alarm system gives rise to abundant benefits such as reducing loss of property or early announcement to fire department. Fire alarm system with fire detectors, smoke detectors or temperature detectors has been widely used to protect property and give warning of fires. However, smoke and temperature detection is slower than light detection, for earlier detection and monitoring the spread of the fire. We design a fire alarm system which consists of simple image processing techniques which alerts us through Wi- Fi internet system. This system implementation helps us to track each and every second moment of fire in our surroundings. Vision based methods were based on color difference, detection of flame pixel and flame edge detection. By identifying color and edge which is generated because of fire pixel and the moment detection of flame which reduces false alarm during fire detection. The strength of using video in fire detection is the ability to monitor large and open spaces. A real time fire detection system is presented which detects fire by analyzing the videos acquired by surveillance cameras. Two main tasks done here: complementary information based on fire features like color, shape variation and motion analysis, are combined by a multi expert system. then a novel descriptor based on a bag-of-words approach has been proposed for representing motion [1]. Vol. 2 (3) June Page 27

2 The dynamic warning process is done in two ways PC internet connection and PC independent GSM modem. This system will be able to take input from the smoke detector system through a signal conditioning circuit. The system will provide alarm at its pre-defined warning stage by controlling the switching conditional circuit. It will automatically update the present status of the communication medium to avoid mishaps during developing fire and identifies the fake fire by comparing the consecutive frame after developing the fire feature set. The comparison is done by measuring change of the size of C i and similarity of color positioning of C i in two consecutive frames [2]. In order to avoid false alarms, effective color and shape-based features are extracted from Candidate Fire Regions. Then, the set of features are fed into the logistic regression to classify the fire and non-fire regions. A randomness test over the features is further adopted for the final fire verification [3]. This paper describes forest fire monitoring and detection strategies using a team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs).A simple elliptical model has been proposed to provide a satisfactory estimation of the perimeter of a free-burning wild land fire[4]. This system uses optical flow features calculated from optical flow vectors created by optical flow estimators i.e., Optical Mass Transport (OMT) and Non-Smooth Data (NSD) for feature vector extraction and then uses trained Back propagation neural networks for feature vector classification [5].This system represents the fire detection algorithm in video sequences on wireless sensor network. Background is statistically modeled by mixture of Gaussians and color detection algorithm was performed in RGB space [6]. Fire flicker is detected by analyzing the video in wavelet domain. Periodic activities of flame are detected by performing temporal wavelet transform. Color variations are identified by computing the spatial wavelet transform of moving fire colored regions [7].Real-time video fire flame and smoke detection method based on foreground image accumulation and optical flow technique is presented. The foreground images which are extracted using frame differential method [8]. For the color information of the flame block, motion is analyzed by the second order color moments in HSV color space, and then it extracts the suspected flame color motion area. For each pixel of the block area, this paper uses spatial continuous images to do wavelet analysis and extracts pixels which match the flame fluctuation rules as the flame pixels. Local weighted operator is used to filter out non-flame discrete pixels, gets the adjacent flame pixels area to form connected areas and sounds an alarm [9]. This system uses HSV and YCbCr color models with given conditions to separate shades of red like orange, yellow, and high brightness light from background and ambient light. Fire growth is analyzed and calculated based on frame differences [10]. YCbCr colour space is used for effectively separating luminance from chrominance.this method separates fire flame pixels and also separates high temperature fire centre pixels by using statistical parameters of fire image in YCbCr colour space like mean and standard deviation. Here four rules are created to separate the true fire region. First two are used for fire region segmentation and other two rules are used for segmentation of high temperature fire centre region [11]. Candidate fire regions in a frame are identified using background subtraction method and color analysis is done based on a nonparametric model. Subsequently, the fire behavior is modeled by using various spatio-temporal features such as color probability, flickering, spatial and spatiotemporal energy, while dynamic texture analysis is applied in each CFR s using linear dynamical systems and a bag of systems approach. At last, a two-class SVM classifier is used to classify the candidate regions [12]. Vol. 2 (3) June Page 28

3 2. FIRE DETECTION ALGORITHM Conventional fire detectors cannot detect characteristic parameters of fire like smoke, temperature, vapor at very early time of fire and cannot meet the demand of early fire detection. Compared to conventional fire detectors, video fire detectors have many advantages such as fast response, long distance of detection, large protection area, early detection are mainly applicable to large rooms and high buildings. But most of existing methods for video fire detection have high rates of false alarms. The proposed real time fire detection algorithm is used to detect the fire flame from the real time surveillance input video through the webcam to the Raspberry pi B+ processor board. Fire-like colours are roughly separated from input video using colour masking process are differentiate the fire pixel from background object pixel. It visually enhanced the each frame of the video to analyse. The conversion of RGB image into Greyscale image used to carry out the colour segmentation analysis and edge detection. If the analysis provides a positive result i.e. the presence of fire on the surveillance region, the raspberry pi will send message to mobile which is connected through Wi-Fi to prevent the fire disasters. Simulation is done by using Python Open CV. Instead of using saved videos for testing, Real time videos captured through the webcam is taken as input for fire analysis. That is., truly real time fire detection is done here. The proposed algorithm has two important steps: color based segmentation and edge based segmentation. Fig 1 Block diagram of Proposed Fire Detection Algorithm. Color Based Segementation Fire has unique features like color, shape and motion. These features vary over time and this behavior makes it as an unusual phenomenon. When fire occurs smoke and flame can be seen. So, in order to detect fire, both flame and smoke can be analyzed. But because of colorless nature, smoke is difficult to analyze. Fire has unique color range. A fire in motion usually have static general shape but with shape of continuously moving. By analyzing color and motion of flame, we can found whether the captured image has fire or not. Fig 2 Block Diagram of Color Detection System Vol. 2 (3) June Page 29

4 Color space is defined as a way by which the specification, creation and visualization of colors is performed. Typically color is represented by three coordinates. The location of the color in the color space is identified by these parameters. Color space conversion is defined as the process of transforming a color from one model to another model. The aim of creating this translated image appears as similar as possible to the original. The commonly used color spaces are RGB, YCbCr and HSV. In the proposed system, the images in RGB color space are converted to grayscale image. Color detection is used to detect any occurrences of fire in a video. Using OpenCV model, a Color Detector model is built based on this block diagram. 1) Grayscale Conversion A Gray scale image is an image in which the value of each pixel is a single sample i.e., it carries only intensity information. The intensity of a pixel is expressed with given range between a minimum and maximum i.e. from black (0) to white (1). Grayscale images for visual display are commonly stored with 8 bits per pixel which allows 256 different intensities (shades of gray). Due to lot of colors present in color image, it is difficult to analyze this color images. It leads to the need of gray conversion. During conversion, all colors (in RGB) are replaced with shades of gray which reduces the complexity and gray scaled image alone used here for thresholding process where we identify presence of fire pixels. Since, it is easy to assign a threshold values for a grayscale image than color image. 2) Masking Mask is a binary of the same dimensions as the original image or the region of interest. The mask is used to restrict the result to the pixels that are selected in the mask. This process is known as Masking. Color masking technique is used to remove the fire-like color region of background from the fire candidate regions. Resultant masked image has only the objects which as fire pixels. The fire objects are displayed as white color and other information on images are in black color i.e. masked image is a binary image. If input image has no fire color range then the masked image has entirely black color. 3) Thresolding The easiest approach to segment an image is thresholding. Thresholding is defined as an operation that involves tests against a function T, T=T[x,y,p(x,y),f(x,y)] Where f(x,y) is the gray level of point (x,y), and p(x,y) denotes some local property of the point such as the average gray level of a neighborhood centered on (x,y).the major problem with thresholding is that we consider the intensity alone. There is no assurance that the pixels recognized by the thresholding process are adjacent. Another problem on global thresholding is that changes in illumination on image may cause some parts to be brighter (in the light) and some parts darker (in shadow) in ways that have nothing to do with the objects in the image. One way to overcome this uneven illumination problem is to first estimate the uneven illumination and then correct it then global thresholding can be employed. Another way is to use adaptive thresholding by dividing the original image into a number of sub images and make use of global thresholding techniques for each sub image. Thus we choose adaptive thresholding for this system for efficient color detection Procedure for Adaptive thresholding is: The threshold value varies over the image as a function of local image characteristics. Image f is divided into sub images. A threshold is determined independently in each sub image. If a threshold can't be determined in a sub image, it can be interpolated with thresholds obtained in neighboring sub images. Vol. 2 (3) June Page 30

5 Each sub image is then processed with respect to its local threshold. Edge Based Segmentation The flames usually display reddish colors. A color model could be built to recognize flames. Unfortunately, some regions or objects in an image may have the same colors as fire, and these areas are usually extracted as the real fire from an image. The red color objects may cause a false extraction of fire-flames. The second reason of wrong fire-identification is that the solar reflections and artificial lights have an important influence on extraction, making the process complex and unreliable. To differentiate between flame and flame-colored objects, the only way is the nature of their physical movement. So to validate a real burning fire, in addition to using color feature, motion features are usually adopted. These fire features include rapid movements of flames, unstable shapes and growing rate. If the edges of an object exhibit rapid time-varying behavior, then this is an important sign of the presence of flame in the scene. A. Canny edge detector The Canny edge detector is an optimal detector with multi-stage algorithm to detect a wide range of edges in images. It was created by John F. Canny in Reasons for using canny detector over other detectors are Low error rate Good localization Minimal response Canny edge detection algorithm can be done by the following steps: Apply Gaussian filter for smoothening the image to remove the noise. Find the intensity gradients of the image. Apply non-maximum suppression to get rid of spurious response to edge detection. Apply double threshold to determine potential edges. Using hysteresis: Finalizing the detection of edges by suppressing all the other edges i.e., weak and not connected to strong edges. 1) Gaussian filter Edge detection results are easily affected by image noise, it is necessary to filter out the noise to prevent false detection caused by noise. For smoothening the image, a Gaussian filter is convolved with the image. This process will slightly smooth the image to reduce the effects of noise on the edge detector. The equation for a Gaussian filter kernel of size (2k+1) (2k+1) is given by: H ij = exp The size of the Gaussian filter will affect the performance of the detector. If the size is larger, the detector s sensitivity to noise will be low. Additionally, if the Gaussian filter kernel size is increased then the localization error will slightly increase. 2) Finding the intensity gradient of the image An edge in an image is a point in a variety of directions, so that canny algorithm uses four filters for detection of points in four directions such as horizontal, vertical and diagonal edges in the blurred image. The edge detection operator gives a value for the first derivative in the horizontal direction and the vertical direction G x and G y respectively. The edge gradient and direction can be founded by: G = and ) Vol. 2 (3) June Page 31

6 Where G can be calculated using the hypot function and tan is the arctangent function with two arguments. The direction angle of edge is rounded to any one of four angles which represents vertical, horizontal and also the two diagonals. An edge direction falling in each color region will be set to a particular angle values. 3) Non-maximum suppression It is an edge thinning technique. Even though the gradient calculation is applied, the extracted edge is still somewhat blurred. With respect to criterion 3, there should only be one accurate response to the edge. Thus this edge thinning technique can help to suppress all the gradient values to 0 except the local maximal, since the local maxima indicates location of sharpest change of intensity value. This process can be achieved by using below steps: The edge strength of the current pixel is compared with the edge strength of the pixel in both positive and negative gradient directions. If the strength of the current pixel is more than the other pixels in the mask with the same direction then the value will be preserved. Otherwise, the value will be suppressed. 4) Double Threshold After applying the non-maximum suppression, the edge pixels are more accurate to present the real edge. But, still some edge pixels are present caused by noise and color variation. So we need to filter the weak edge pixel gradient value and preserve the edge with the high gradient value. These two threshold values are used to check the different types of edge pixels, the higher gradient value is called high threshold value and the weak gradient value is called the low threshold value. The edge pixel s gradient value which is higher than the high threshold value, are known as strong edge pixels. The edge pixel s gradient value which is smaller than the high threshold value and larger than the low threshold value, are known as weak edge pixels. The pixel value which is smaller than the low threshold value, they will be suppressed 5) Edge tracking by hysteresis To get an accurate result, the weak edges should be removed. Usually while noise responses are unconnected, a weak edge pixel from true edges will be connected to a strong edge pixel. To find the edge connection, blob analysis is applied by identifying at a weak edge pixel and its 8-connected neighborhood pixels. Until there is one strong edge pixel present in the blob, that weak edge point can be founded as one that should be preserved. 3. The Hardware details Raspberry Pi The Raspberry Pi is the work of the Raspberry Pi Foundation, a charitable organization UK registered charity (No ), May 2009 and Its supported by the University of Cambridge Computer Laboratory and tech firm Broadcomm. The Raspberry Pi is a credit card sized fully featured microcomputer squashed onto a circuit board measuring approximately 9cm x 5.5cm Features of Raspberry Pi3: The Raspberry Pi has a Broadcomm BCM2835 system on chip (SoC), which include an 1.2GHz 64-bit quad-core ARMv8 CPU and it has Video Core IV GPU Originally shipped with 512MB of RAM, Later upgraded to 1GB and It does not include a built-in hard disk, but uses an SD card for booting and long-term storage. It has inbuilt Wi-Fi and also supports 10/100 Base Ethernet socket It has HDMI socket, USB 2.0 socket, RCA video socket, SD card socket and 3.5mm Audio out jack Powered from micro USB socket Vol. 2 (3) June Page 32

7 Header Footprint for camera connection By default, Supporting Python and operating system - Linux on a bootable SD card like Raspbian. Webcam Fig 3 Layout diagram of Raspberry Pi A webcam is a video camera that captures the images in real time and feeds to a computer or network which is usually connected by a USB cable. Specification of Webcam: Optical lens with CMOS sensor 25 Megapixel (Interpolated) and Frame rate up to 30fps Video resolution: 320x240, 640x480 and also provides HD image resolution (Interpolated): from 1600 x 1200, upto6048 x built-in LEDs and USB 2.0 interface Support Windows XP SP2/VISTA/7 and also supports LINUX Kernel version. 4. Results Vol. 2 (3) June Page 33

8 Fig 4 The Captured image (Input) Fig 5 Binary image Fig 6 & 7 shows the image which edges found during the canny edge segementation process and the final ouput with highligthed fire regions respectively. Fig 6 Result of Raspberry pi after edge detection Vol. 2 (3) June Page 34

9 Fig 7 Output image after color and edge detection Fig 8 shows the hardware setup of python based fire detection with the Raspberry Pi board. Fig 8 Fire Detection system with Raspberry Pi B+ Board Conclusion The proposed project has presented the quick analysis of the fire image. The simplest fire detection algorithm is proposed, which is free from the ordinary fire detection systems which are consisting of number of sensors. The objectives of this project were to create a system which would be able to detect flame using images from a webcam connected with RASPBERRY PI board was achieved. The system was made to detect flame while they are still small and have not grown too large. System is using Open Source Computer Vision Library, also known as Open CV, is an open source freeware. In order to reduce false detection, color detection followed by edge detection will be done. This system results are in real time and it provides better performance than MATLAB in term of fewer false alarms and thus a higher system performance is achieved. REFERENCES [1] Pasquale Foggia,Alessia Saggese and Mario Vento, Real time fire detection using video surveillance applications using a combination of experts based on color, shape and motion in IEEE Transactions on Circuits and Systems for Video Technology,2015. [2] Md. Mahamudul Hasan, M. Abdur Razzak, An Automatic Fire Detection and Warning System Under Home Video Surveillance in IEEE 12th International Colloquium on Signal Processing & its Applications, [3] Donglin Jin, Shengzhe Li, Hakil Kim, Robust fire detection using logistic regression and randomness testing for real-time video surveillance in IEEE 10th Conference on Industrial Electronics and Applications, [4] Khaled A. Ghamry, Mohamed A. Kamel, and Youmin Zhang, Cooperative Forest Monitoring and Fire Detection Using a Team of UAVs UGVs in IEEE International Conference on Unmanned Aircraft Systems, [5] S.E.Memane, V.S.Kulkarni, A Review on Flame and Smoke detection techniques on videos, International Journal of Advanced Research in Electrical,Electronics and Instrumentation Engineering, [6] Yoon-Ho Kim, Alla Kim, and Hwa-Young Jeong, RGB Color Model Based the Fire Detection Algorithm in Video Sequences on Wireless Sensor Network in International Journal of Distributed Sensor Networks, Vol. 2 (3) June Page 35

10 [7] Yigithan Dedeoglu, B. Ugur Toreyin, Ugur Gudukbay, A. Enis Cetin, Real-time fire and flame detection in video in IEEE Conference, [8] Chunyu Yu, Zhibin Mei, Xi Zhang, A real-time video fire flame and smoke detection algorithm in International Association for Fire Safety Science Conference, [9] Lei Chen and Ji-Feng Huang, Flame Detected In Video Based On Color Moments in IEEE 11th International Computer Conference on Wavelet Active Media Technology and Information Processing, [10] Jareerat Seebamrungsat, Suphachai Praising, and Panomkhawn Riyamongkol, Fire Detection in the Buildings Using Image Processing in IEEE Third ICT International Student Project Conference, [11] C. Emmy Prema1 and S. S. Vinsley, Image Processing Based Forest Fire Detection using YCbCr Colour Model in International Journal of Emerging Technology and Advanced Engineering, [12] Kosmas Dimitropoulos, Panagiotis Barmpoutis and Nikos Grammalidis, Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection in IEEE Transactions on Circuits and Systems for Video Technology, Vol. 2 (3) June Page 36