Abstract
As fire accidents usually cause economic and environmental damage, including the loss of human lives, video-based fire detection has become more appealing in surveillance systems. However, video based fire detection algorithms demand tremendous computational and I/O requirements. To meet these requirements, we introduce an SIMD (Single Instruction Multiple Data) based multi-core architecture that consists of 16 processing elements (PEs) and small local memory. In addition, we compare the performance and efficiency of the multi-core architecture with a commercial Texas Instrument digital signal processor (TI DSP) to demonstrate the potential for improved performance of the multi-core architecture. Experimental results indicate that the multi-core architecture is 27.18 times and 3.89 times better than TI DSP in terms of execution time and energy efficiency, respectively.
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References
Celik, T., Demirel, H.: Fire Detection in Video Sequences Using A Generic Color Model. Fire Safety J. 44, 147–158 (2009)
Celik, T., Qzkaramanh, H., Demirel, H.: Fire And Smoke Detection without Sensors: Image Processing Based Approach. In: 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, pp. 1794–1798 (2007)
Chen, T.H., Wu, P.H., Chiou, Y.C.: An Early Fire-Detection Method Based on Image Processing. In: IEEE International Conference on Image Processing, vol. 3, pp. 1707–1710 (2004)
Toreyin, B.U., Centin, A.E.: Online Detection of Fire in Video. In: IEEE International Conference on Computer Vision and Pattern Recognition, Minneapolis, pp. 1–5 (2007)
Ko, B.C., Cheong, K.H., Nam, J.Y.: Fire Detection Based on Vision Sensor and Support Vector Machines. Fire Safety J. 44, 322–329 (2009)
Nguyen, H., John, L.: Exploiting SIMD Parallelism in DSP and Multimedia Algorithms Using the AltiVec Technology. In: International Conference on Supercomputing (ICS 1999), Rhodes, pp. 11–20 (1999)
Abbo, A.A., Kleihorst, R.P., Choudhary, V., Sevat, L., Wielarge, P., Mouy, S., Vermeulen, B., Heijligers, M.: Xetal-II: A 107 GOPS, 600mW Massively Parallel Processor for Video Scene Analysis. IEEE J. Solid-State Circuits 43(1), 192–201 (2008)
Chhugani, J., Nguyen, A.D., Lee, V.W., Macy, W., Hagog, M., Chen, Y.K., Baransi, A., Kumar, S., Dubey, P.: Efficient Implementation of Sorting on Multi-Core SIMD CPU Architecture. In: International Conference on Very Large Data Bases, pp. 1313–1324 (2008)
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Kim, Y., Kang, M., Kim, JM. (2012). High-Performance Video Based Fire Detection Algorithms Using a Multi-core Architecture. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_62
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DOI: https://doi.org/10.1007/978-3-642-25944-9_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25943-2
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