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Contour-based smoky vehicle detection from surveillance video for alarm systems

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Abstract

Existing smoky vehicle detection methods and related smoke detection methods still have high false alarm rates. To solve this issue, this paper presents a contour-based smoky vehicle detection method for alarm systems. In this method, the hybrid background estimation method and some rules are adopted to detect vehicle objects. Three groups of artificial features based on the contour of the vehicle object are designed and extracted to distinguish between smoky vehicles and non-smoky vehicles. More specifically, by analysing the contour and convex hull at the back of the vehicle, some representative features, including the static features based on the contour and convex hull; statistical features of the mean, variance, skewness and kurtosis in the potential smoky region; and dynamic features of contour matching based on Hu moments, are designed and fused for smoky vehicle recognition. The support vector machine is adopted to classify the extracted features. The experimental results show that the proposed method can achieve better performance and lower false alarm rates than existing methods.

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References

  1. Tahir, M., Hussain, T., Behaylu, A.: Evaluation of growth of motor vehicles fleet and ambience air quality in India. Civ. Environ. Res. 7(6), 41–46 (2015)

    Google Scholar 

  2. Walsh, M.P.: PM 2.5: global progress in controlling the motor vehicle contribution. Frontiers of. Environ. Sci. Eng. 8(1), 1–17 (2014)

    MathSciNet  Google Scholar 

  3. Peng, Y., Chen, Z., et al.: Traffic flow detection and statistics via improved optical flow and connected region analysis. SIViP 12(1), 1–7 (2017)

    MathSciNet  Google Scholar 

  4. Tao, H., Lu, X.: Smoky vehicle detection based on multi-feature fusion and ensemble neural networks. Multimedia Tools and Applications (2018). https://doi.org/10.1007/s11042-018-6248-2

    Google Scholar 

  5. Pyykonen, P., Peussa, P., Kutila, M., et al.: Multi-camera-based smoke detection and traffic pollution analysis system. In: Proceedings of the International Conference Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, pp. 233–238(2016)

  6. Tao, H., Lu, X.: Smoky vehicle detection based on multi-scale block Tamura features. Signal, Image and Video Processing. (2018). https://doi.org/10.1007/s11760-018-1254-4

    Google Scholar 

  7. Barmpoutis, P., Dimitropoulos, K., Grammalidis, N.: Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition. In: Proceedings of the International Conference European Signal Processing Conference (EUSIPCO), IEEE, pp. 1078–1082 (2014)

  8. Morerio, P., Marcenaro, L., Regazzoni, C.S., Gera, G.: Early fire and smoke detection based on colour features and motion analysis. In: Proceedings of the Conference IEEE International Conference on Image Processing, IEEE, pp. 1041–1044 (2012)

  9. Liu, Z.G., Yang, Y., Ji, X.H.: Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space. SIViP 10(2), 277–284 (2016)

    Article  Google Scholar 

  10. Nguyen, T.K.T., Kim, J.M.: Multistage optical smoke detection approach for smoke alarm systems. Opt. Eng. 52(8), 057001 (2013)

    Article  Google Scholar 

  11. Yuan, F.: A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recogn. Lett. 29(7), 925–932 (2008)

    Article  Google Scholar 

  12. Gunay, O., Toreyin, B.U., Kose, K., et al.: Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video. IEEE Trans. Image Process. 21(5), 2853–2865 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Töreyin, B.U., Dedeoğlu, Y., Cetin, A.E., Wavelet based real-time smoke detection in video. In: Proceedings of the International Conference European Signal Processing Conference, pp 1–4 (2005)

  14. Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Contour based smoke detection in video using wavelets. In: Proceedings of the International Conference European Signal Processing Conference, pp. 1–5 (2006)

  15. Toreyin, B.U., Cetin, A.E.: Computer vision based forest fire detection. In: Proceedings of the International Conference on Signal Processing, Communication and Applications. Siu. IEEE. IEEE, pp. 1–4 (2008)

  16. Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Real-time smoke and flame detection in video, pp. 695–698. Proc. Int. Conf. Signal Process. Commun. Appl., IEEE (2006)

    Google Scholar 

  17. Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Flame detection in video using hidden Markov models. IEEE International Conference on Image Processing 2, 1230–1233 (2005)

    Google Scholar 

  18. Dedeoglu, N., Toreyin, B.U., Gudukbay, U., Cetin, A.E.: Real-time fire and flame detection in video. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. IEEE, pp. 669–672 (2005)

  19. Yuan, F.: A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection. Pattern Recogn. 45(12), 4326–4336 (2012)

    Article  Google Scholar 

  20. Tian, H., Li, W., Ogunbona, P.O., et al.: Detection and Separation of Smoke from Single Image Frames. IEEE Trans. Image Process. 99, 1–14 (2018)

    MathSciNet  Google Scholar 

  21. Tian, H., Li, W., Wang, L., Ogunbona, P.: A novel video-based smoke detection method using image separation. In: Proceedings of the International Conference on Multimedia and Expo, pp. 532–537(2012)

  22. Tian, H., Li, W., Wang, L., Ogunbona, P.: Smoke detection in video: an image separation approach. Int. J. Comput. Vision 106(2), 192–209 (2014)

    Article  Google Scholar 

  23. Collins, R.T., Lipton, A.J., Kanade, T., et al.: A system for video. Surveill. Monit. 59(5), 329–337 (2000)

    Google Scholar 

  24. Heijden, F.V.D.: Image based measurement systems: object recognition and parameter estimation. Phys. Med. Biol. 32(2), 194–194 (1994)

    Google Scholar 

  25. Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. CVGIP 30(1), 32–46 (1985)

    MATH  Google Scholar 

  26. Alan, S.: Topologically consistent line simplification with the Douglas-Peucker algorithm. Cartogr. Geogr. Inf. Sci. 30(1), 32–46 (1985)

    Google Scholar 

  27. Sklansky, J.: Finding the convex hull of a simple polygon. Pattern Recogn. Lett. 1(2), 79–83 (1982)

    Article  MATH  Google Scholar 

  28. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8, 179–187 (1962)

    MATH  Google Scholar 

  29. Khan, N.M., Ksantini, R., Ahmad, I.S., Guan, L.: SN-SVM: a sparse nonparametric support vector machine classifier. SIViP 8(8), 1625–1637 (2014)

    Article  Google Scholar 

  30. Beger, A.: Precision-Recall Curves. Social Science Electronic Publishing, Rochester (2016)

    Book  Google Scholar 

  31. Gubbi, J., Marusic, S., Palaniswami, M.: Smoke detection in video using wavelets and support vector machines. Fire Saf. J. 44(8), 1110–1115 (2009)

    Article  Google Scholar 

  32. Yuan, F.: Video-based smoke detection with histogram sequence of LBP and LBPV pyramids. Fire Saf. J. 46(3), 132–139 (2011)

    Article  Google Scholar 

  33. Wang, S., He, Y., Yang, H., et al.: Video smoke detection using shape, color and dynamic features. J. Intell. Fuzzy Syst. 33(1), 305–313 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61374194), National Key Science and Technology Pillar Program of China (No. 2014BAG01DB03), Key Research and Development Program of Jiangsu Province (No. BE2016739), the Scientific Research Foundation of Graduate School of Southeast University, the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX18_0101) and the State Scholarship Fund from China Scholarship Council.

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Huanjie Tao & Xiaobo Lu

  2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, China

    Huanjie Tao & Xiaobo Lu

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  1. Huanjie Tao
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  2. Xiaobo Lu
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Correspondence to Xiaobo Lu.

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Tao, H., Lu, X. Contour-based smoky vehicle detection from surveillance video for alarm systems. SIViP 13, 217–225 (2019). https://doi.org/10.1007/s11760-018-1348-z

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  • DOI: https://doi.org/10.1007/s11760-018-1348-z

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