Post on 22-Feb-2017
FIRE DETECTION USING GRAYSCALE VIDEO PROCESSING FOR BLACK AND WHITE
CAMERASby
GAURAV KISHOR DESHMUKH
Leonard Brown, PhD., Committee ChairDepartment of Computer Science
May 1, 2023Research Project Presentation
Research Committee:Dr. Leonard Brown IIIDr. Arun KulkarniDr. Naryanan Subramanian
Overview
Motivation Literature Review Proposed System Overview Performance Evaluation Conclusion
May 1, 2023Research Project Presentation
Motivation
May 1, 2023
Human and Economic Loss
Reasons for failure of Traditional Fire sensors Locality of fire Fail to detect potential range of fire
Visually Detectable Fire Characteristics Fire Color Motion Self Luminosity Flickering Smoke
Research Project Presentation
Overview
Motivation Literature Review Proposed System Overview Performance Evaluation Conclusion
May 1, 2023Research Project Presentation
May 1, 2023Research Project Presentation
Literature Review Core Component of the color based fire detection techniques
Fire has yellowish - red color
Various color models to analyze colors in video sequences or images Red Green Blue (RGB)
• Concentrates more on red color• Ignores brightness • Potentially can detect any red object as fire• Is based on experimental threshold
May 1, 2023Graduate Seminar Presentation
Literature Review Hue Saturation Intensity (HSI)
• Concentrates more on hues• Considers brightness but ignores luminosity• Is based on experimental threshold
YCbCr • Considers luminosity• Luminosity is a factor of basic Green (G)• Can potentially detect any greenish object as fire
May 1, 2023Graduate Seminar Presentation
Literature Review Mixture Models
• Are composed of combinations of color model or mathematical transforms of same
• Gaussian RGB mixtureo Constructed using frequency transform on RGBo Improves hue of fire o Fails to consider self luminosity feature
• RGB + HSIo Others hues of red and brightness are consideredo Is based on threshold
Literature Review Flickering is another important characteristic of fire It distinguishes the real fire from image of fire Motion can be detected in video sequences using :
Temporal Differencing• Consecutive frames in the videos are subtracted to detect
motion• Detects every different pixel from the video
May 1, 2023Graduate Seminar Presentation
Literature Review Background Differencing
• Static background is subtracted from number of frames• Detects only moving pixel , fails to give sense of direction
Optical Flow Method• Motion and direction of each and every pixel in the frame is
considered• With the direction , Potential range of fire spread can be
calculated
May 1, 2023Graduate Seminar Presentation
Literature Review In any detection algorithm , False rate needs to be minimized For minimization some kind of decision mechanism is used Decision mechanisms surveyed are :
Neural Networks• Trained neural networks reduces rate of false detected pixels
drastically Fuzzy Logic
Rule based detection will help us determine true fire pixel Decision mechanisms are supportive
• Does not work if input given to it is faultyMay 1, 2023Graduate Seminar Presentation
Overview
Motivation Literature Review Proposed System Overview Performance Evaluation Conclusion
May 1, 2023Research Project Presentation
Proposed System Overview
May 1, 2023Research Project Presentation
CCTV VideoB/W
Conversion Module
Fire Detector Module
Alarm
Proposed System Overview
May 1, 2023Research Project Presentation
CCTV Video
B/W Conversion
Module
Fire Detector Module
Alarm
Proposed System Overview
May 1, 2023Research Project Presentation
Video I/P
CCTV Video
Module
Multiple FramesSingle Frame
To next module
CCTV Video Module
Proposed System Overview
May 1, 2023Research Project Presentation
CCTV Video Module
Proposed System Overview
May 1, 2023Research Project Presentation
CCTV VideoB/W
Conversion Module
Fire Detector Module
Alarm
Proposed System Overview
May 1, 2023Research Project Presentation
cvCvtColor() function
Frame
Pixel in given frameRGB Components
Corresponding gray pixel
B/W Conversion Module
Proposed System Overview
May 1, 2023Research Project Presentation
B/W Conversion Module
B/W Conversion
Module
Proposed System Overview
May 1, 2023Research Project Presentation
CCTV VideoB/W
Conversion Module
Fire Detector Module
Alarm
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Frame Analysis Pixel Analysis Classifier
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Frame Analysis Pixel Analysis Classifier
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Frame Analysis
Background Subtraction
Frame (N) Frame Difference
= - Frame (N-1)
Proposed System Overview
May 1, 2023Research Project Presentation
Background Subtraction
fire pixels in motion
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Frame Analysis
Background Subtraction Contour Analysis
Proposed System Overview
May 1, 2023Research Project Presentation
Contour Analysis
Boundary of pixels in motion
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Frame Analysis
• Output of overall frame
analysis is obtained by performing
AND operation between frames
obtained from background subtraction
and contour analysis component
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Frame Analysis Pixel Analysis Classifier
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Pixel Analysis
• For performing pixel analysis Canny edge detection algorithm is used [16]
• Helps in detection of fractal like objects
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Pixel Analysis
• Output of pixel analysis is obtained by performing AND operation between frame analysis output and Canny edge detector output
Potential candidate fire pixels
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Frame Analysis Pixel Analysis Classifier
Proposed System Overview
May 1, 2023Research Project Presentation
Fire Detector Module Classifier
• This component further filters out the candidate pixels• Each pixel in obtained frame is compared to threshold value• The literature suggests this value should be greater than 180• The system uses three threshold values
1. Brightness Threshold
2. Canny Threshold
3. Flicker Threshold
Overview
Motivation Literature Review Proposed System Overview Performance Evaluation Conclusion
May 1, 2023Research Project Presentation
Performance Evaluation
May 1, 2023Research Project Presentation
Demonstration
Performance Evaluation
May 1, 2023Research Project Presentation
Metrics Precision Recall Accuracy Time elapsed to detect first positive frame
Static Parameters Flicker Threshold Canny Threshold Number of Frames Resolution of given video Frames per second for given video
Dynamic Parameters Brightness threshold
Performance Evaluation
May 1, 2023Research Project Presentation
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 2550
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
TEST CASE 1
Precision
Recall
Accuracy
Bright Threshold
Prec
isio
n - R
ecal
l -Ac
cura
cy
Performance Evaluation
May 1, 2023Research Project Presentation
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 2550
50000
100000
150000
200000
250000
300000
350000
TEST CASE 1
Time Taken (msec)
Bright Threshold
Tim
e Ta
ken
to d
etec
t fir
st p
osit
ive
fram
e (m
sec)
Overview
Motivation Literature Review Proposed System Overview Performance Evaluation Conclusion
May 1, 2023Research Project Presentation
Conclusion
May 1, 2023Research Project Presentation
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 2550
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Avg. Precision
Avg.Recall
Avg. Accuracy
Bright Threshold
Avg.
Pre
cisi
on -
Avg.
Reca
ll - A
vg. A
ccur
acy
Conclusion
May 1, 2023Research Project Presentation
System is showing average accuracy of 60% Precision and recall shows downward slope with increase
in threshold values The assumption of fire pixels having gray value more than
180 has proved wrong Equalization of histogram for each frame may prove
beneficial in improving the precision and recall of the system
May 1, 2023Research Project Presentation
Thank You
References
May 1, 2023Research Project Presentation
[1] Y. Do, “Flame detection in grey-scale images of a B/W camera”, in Sensor Review, vol. 34, no. 1, pp. 80–88, 2014.
[2] H. Yamagishi and J. Yamaguchi, “A contour fluctuation data processing method for fire flame detection using a color
camera”, in Industrial Electronics Society 26th Annual Confjerence of the IEEE, IEEE, 2000, vol. 2, pp. 824–829.
[3] L. Hongliang, L. Qing, and W. Sun’an, “A novel fire recognition algorithm based on flame’s Multi-features Fusion,”
in International Conference on Computer Communication and Informatics (ICCCI), 2012, pp. 1–6.
[4] F. Yuan, “An integrated fire detection and suppression system based on widely available video surveillance”, in Machine
Vision and Applications, 2010, vol. 21, no. 6, pp. 941–948.
[5] X. Zhou, F. Yu, Y. Wen, Z. Lu, and G. Song, “Early fire detection based on flame contours in video”, in Information
Technology Journal, 2010, vol. 9, no. 5, pp. 899–908.
[6] A. Çetin, K. Dimitropoulos, B. Gouverneur, N. Grammalidis, O. Günay, Y. Habiboglu, B. Töreyin, and S. Verstockt,
“Video fire detection–Review”, in Digital Signal Processing, 2013, vol. 23, no. 6, pp. 1827–1843.
[7] R. Bohush and N. Brouka, “Smoke and flame detection in video sequences based on static and dynamic features,”
in Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) , 2013, pp. 20–25.
[8] T. Song, B. Tang, M. Zhao, and L. Deng, “An accurate 3-D fire location method based on sub-pixel edge detection
and non-parametric stereo matching”, in Measurement, 2014.
References
May 1, 2023Research Project Presentation
[9] T. Li, M. Ye, F. Pang, H. Wang, and J. Ding, “An efficient fire detection method based on orientation feature” in International Journal of
Control, Automation and Systems, 2013, vol. 11, no. 5, pp. 1038–1045.
[10] L. Mengxin, X. Weijing, Z. Ying, and Z. Rui, “A new fire detection method based on integrated features,” in 24th Chinese Control and
Decision Conference (CCDC), 2012, pp. 965–970.
[11] M. Mueller, P. Karasev, I. Kolesov, and A. Tannenbaum, “Optical Flow Estimation for Flame Detection in Videos”, in IEEE
Transactions on Image Processing, 2013, vol. 22, no. 7, pp 2786 – 2797.
[12] P. Borges and E. Izquierdo, “A probabilistic approach for vision-based fire detection in videos”, in IEEE Transactions on Circuits and
Systems for Video Technology,IEEE, 2010 , vol. 20, no. 5, pp. 721–731.
[13] G. Marbach, M. Loepfe, and T. Brupbacher, “An image processing technique for fire detection in video images” in Fire safety
journal, 2006, vol. 41, no. 4, pp. 285–289.
[14] B. Ko, K. Cheong, and J. Nam, “Fire detection based on vision sensor and support vector machines” in Fire Safety
Journal, 2009, vol.44, no. 3, pp. 322–329.
[15] R. Lascio, A. Greco, A. Saggese and M. Vento, “Improving fire detection reliability by a combination of videoanalytics”, International
Conference on Image Analysis and Recognition (ICIAR), 2014.
[16] J. Canny, “A Computational Approach to Edge Detection”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE,
1986, vol. 8, no. 6, pp. 679-698.
[17] The OpenCV Reference Manual, Revision 2.4.9.0, OpenCV Community, April 2014.