Machine Learning Techniques for Video-Based Fire and Smoke Detection

1. UNIVERSIRTY OF ULSAN School of Computer Engineering & Information Technology Embedded System Laboratory Machine Learning Techniques forVideo-Based Fire and Smoke Detection Presenter : Truong Xuan Tung Advisor : Jong-Myon Kim Laboratory: Embedded System Laboratory

2. Introduction Video Fire and Smoke Detection Image Segmentation Fire and Smoke Detection Systems Image Segmentation Systems Fire and Smoke Alarm

3. Part I ProposedFire and Smoke Detection Algorithms • Motivation • Proposed Algorithms • Experimental Results • Conclusion

4. Motivation • Nowadays, several fire and smoke detection algorithms have been proposed. However, these algorithms have limited application and lacked robustness. • This study proposed effective algorithms that can detect fire and smoke automatically in video sequences, in order to improve the performance of the fire alarm systems

5. Proposed Algorithms • The Block Diagram of the Proposed Algorithm Movies Fire Smoke Image Sequences Moving Region Detection Color Segmentation Parameters Extraction Classification Making the Alarm FireAlarm Smoke Alarm

6. Proposed Algorithms • Moving Region Detection Movies Fire Smoke + Adaptive Mixture of Gaussian Model + Diminish the effect of small repetitive motions. + Morphological Operators + Approximated Median Method + Time cost + Memory requirement + Morphological Operators Image Sequences Moving Region Detection Color Segmentation ParametersExtraction Results Classification Making the Alarm System

7. Proposed Algorithms • Color Segmentation Movies Fire Smoke + Fuzzy c-means clustering. + Convert RGB to CIE LAB color space. + Choose the candidate regions. Image Sequences Moving Region Detection + Clusters + Center of clusters FCM clustering A, B Color Segmentation ParametersExtraction Results Classification Making the Alarm System

8. Proposed Algorithms • Parameters Extraction Movies Fire Smoke • Characteristics of the candidate regions • 1. Texture • 2. Contour (Boundary) • 3. Area • 4. Motion Vector Image Sequences Moving Region Detection Color Segmentation Parameters Extraction Classification Making the Alarm System

9. Proposed Algorithms • Parameters Extraction Movies Fire Smoke + Area Randomness + Contour Roughness + Surface Coarseness + Motion Estimation => 17 Feature Parameters + Area Randomness + Background Blurring + Surface Coarseness + Motion Estimation => 22 Feature Parameters Image Sequences Moving Region Detection Color Segmentation Parameters Extraction Classification Making the Alarm System

10. Proposed Algorithms • Fire and Smoke Identification Movies Fire Smoke + Identify fire or non-fire + Back-propagation neural networks (BPNN) + Identify smoke or non- Smoke + Support vector machine (SVM) Image Sequences Moving Region Detection + Smoke + Non-Smoke + Fire + Non-Fire SVM BPNN 22 Parameters 17 Parameters Color Segmentation Parameters Extraction Classification Fire Smoke Making the Alarm System

11. Proposed Algorithms • The Flowchart of the Proposed Algorithms

12. Experimental Results • The Performance Comparison • To compare the performance of proposed algorithm with conventional algorithm, PTP and PTN parameters is utilized in this study. • PTP is the percentage of the overall fire detected frames with positive video • PTN is the percentage of the overall fire detected frames with negative video

13. Experimental Results • Examples of Test Movies (Fire)

14. Experimental Results • The Performance Comparison (Fire)

15. Experimental Results • The Performance Comparison (Fire)

16. Experimental Results • Examples of Test Movies (Smoke)

17. Experimental Results • The Performance Comparison (Smoke)

18. Experimental Results • The Performance Comparison (Smoke)

19. Conclusion • In this study, we proposed effective, four-stage fire and smoke detection algorithms that can detect fire and smoke automatically in video. • Machine learning techniques are applied to each step of the proposed algorithms to improve the performance. • Experimental results indicate that the proposed method outperforms existing fire and smoke detection algorithms, providing high reliability and low false alarm rate

20. Part II • Motivation • Proposed Algorithm • Experimental Results • Conclusion ProposedImage Segmentation Technique

21. Motivation • Image segmentation plays a critical role in image understanding, pattern recognition, and computer vision. • FCM algorithm is an effective technique for image segmentation. However, the performance of the standard FCM is low when the image have noise, low contrast, or blur boundaries. • This study propose an Enhanced Spatial FCM (ESFCM).

22. Proposed ESFCM Algorithm • Conventional FCM algorithm

23. Proposed ESFCM Algorithm • Main Idea • One of the most important characteristics of an image is that the neighboring pixels are highly correlated. • Exploit the spatial information of the image by considering the influence of neighboring pixels on the center pixel

24. Proposed ESFCM Algorithm • Compute Effective Factor Pik • Propose an effective factor that incorporates both information about the spatial position and gray value of the neighboring pixels.

25. Proposed ESFCM Algorithm • Compute New Membership and Centroid Value

26. Experimental Results • Cluster Validity Functions • Partition coefficient • Partition entropy • Xie-Beni function

27. Experimental Results • The Results of the Algorithms with 4 Clusters

28. Experimental Results • The Performance Comparison

29. Conclusion • An enhanced spatial FCM (ESFCM) algorithm is proposed, in order to achieve good clustering performance for images. • ESFCM makes use of the influence of the neighboring pixels on the center pixel by assigning weights to the neighbors. • Experimental results indicate that proposed algorithm outperforms Conventional FCM algorithm

30. Future Works • Apply machine learning techniques to each step of the fire and smoke detection algorithms • Investigate the characteristics of the fire and smoke, and apply different techniques to extracting feature parameters of the fire and smoke. • Innovate techniques applied to existing image segmentations, in order to improve the efficiency of the image analysis methodologies. • Employ these algorithms on various parallel processor architectures such as Single Instruction, Multiple Data streams (SIMD) architecture to improve the speed of the systems.

31. Publications

32. Thank you for your attention Question & Answer