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Journal of Intelligent Systems and Internet of Things
Volume 5 , Issue 2, PP: 54-61 , 2021 | Cite this article as | XML | Html |PDF

Title

Automated Deep Learning based Video Summarization Approach for Forest Fire Detection

  Saeed M. Aljaberi 1 ,   Ahmed N. Al-Masri 2 *

1  Artificial Intelligence Department, Dubai Police, Dubai, UAE
    (eljabri@live.com)

2  American University in the Emirates, Dubai, UAE
    (ahmed.almasri@aue.ae)

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Doi   :   https://doi.org/10.54216/JISIoT.050201

Received: December 19, 2020 Accepted: August 11, 2021

Abstract :

Due to the exponential increase in video data, an automated examination of videos has become essential. A significant requirement is the capability of the automated video summarization process, which is helpful in vast application areas from surveillance to security. It assists in monitoring the user application with reduced memory and time. Therefore, this paper designs an automated deep learning-based video summarization approach for forest fire detection (ADLVS-FFD). The ADLVS-FFD technique aims to summarize the captured videos and detects the existence of forest fire in it. In addition, the ADLVS-FFD technique involves different subprocesses such as frame splitting, feature extraction, and classification. Besides, a merged Gaussian mixture model (MGMM) is used to extract keyframes and features. Moreover, the long short-term memory (LSTM) model is employed to detect and classify input images into normal and forest fire images. To ensure the better performance of the ADLVS-FFD technique, a comprehensive experimental validation process takes place on a benchmark video dataset. The resultant experimental validation process highlighted the supremacy of the ADLVS-FFD technique over the recent methods. 

Keywords :

Video summarization , Deep learning , LSTM Model , Forest fire detection , Feature extraction

References :

References

[1]      He, X., Hua, Y., Song, T., Zhang, Z., Xue, Z., Ma, R., Robertson, N. and Guan, H., 2019, October. Unsupervised video summarization with attentive conditional generative adversarial networks. In Proceedings of the 27th ACM International Conference on Multimedia (pp. 2296-2304).

[2]      Rani, S. and Kumar, M., 2020. Social media video summarization using multi-Visual features and Kohnen's Self Organizing Map. Information Processing & Management, 57(3), p.102190.

[3]      Khan, G., Jabeen, S., Khan, M.Z., Khan, M.U.G. and Iqbal, R., 2020. Blockchain-enabled deep semantic video-to-video summarization for IoT devices. Computers & Electrical Engineering, 81, p.106524.

[4]      Singh, G., Singh, N. and Kumar, K., 2019. PICS: a novel technique for video summarization. In Machine Intelligence and Signal Analysis (pp. 411-421). Springer, Singapore.

[5]      Elharrouss, O., Al-Maadeed, N. and Al-Maadeed, S., 2019, June. Video summarization based on motion detection for surveillance systems. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 366-371). IEEE.

[6]      Chi, R.; Lu, Z.M.; Ji, Q.G. Real-time multi-feature based fire flame detection in video. IET Image Process. 2016, 11, 31–37

[7]      Evarts, B. Fire loss in the United States during 2017; National Fire Protection Association, Fire Analysis and Research Division: Quincy, MA, USA, 2018

[8]      Wei, H., Ni, B., Yan, Y., Yu, H., Yang, X. and Yao, C., 2018, April. Video summarization via semantic attended networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).

[9]      Ji, Z., Ma, Y., Pang, Y. and Li, X., 2019. Query-aware sparse coding for web multi-video summarization. Information Sciences, 478, pp.152-166.

[10]   John, A.A., Nair, B.B. and Kumar, P.N., 2017, April. Application of clustering techniques for video summarization–an empirical study. In Computer Science On-line Conference (pp. 494-506). Springer, Cham.

[11]   Ji, Z., Xiong, K., Pang, Y. and Li, X., 2019. Video summarization with attention-based encoder–decoder networks. IEEE Transactions on Circuits and Systems for Video Technology, 30(6), pp.1709-1717.

[12]   Yuan, L., Tay, F.E.H., Li, P. and Feng, J., 2019. Unsupervised video summarization with cycle-consistent adversarial LSTM networks. IEEE Transactions on Multimedia, 22(10), pp.2711-2722.

[13]   Varini, P., Serra, G. and Cucchiara, R., 2017. Personalized egocentric video summarization of cultural tour on user preferences input. IEEE Transactions on Multimedia, 19(12), pp.2832-2845.

[14]   Yasmin, G., Chowdhury, S., Nayak, J., Das, P. and Das, A.K., 2021. Key moment extraction for designing an agglomerative clustering algorithm-based video summarization framework. Neural Computing and Applications, pp.1-22. 

[15]   Basavarajaiah, M. and Sharma, P., 2021. GVSUM: generic video summarization using deep visual features. Multimedia Tools and Applications, 80(9), pp.14459-14476.

[16]   Davids, D.M. and Christopher, C.S., 2021. An efficient video summarization for surveillance system using normalized k-means and quick sort method. Microprocessors and Microsystems, 83, p.103960. 

[17]   Messaoud, S., Lourentzou, I., Boughoula, A., Zehni, M., Zhao, Z., Zhai, C. and Schwing, A.G., 2021. DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization. arXiv preprint arXiv:2105.06441. 

[18]   Parihar, A.S., Pal, J. and Sharma, I., 2021. Multiview video summarization using video partitioning and clustering. Journal of Visual Communication and Image Representation, 74, p.102991. 

[19]   Zivkovic, Z., 2004, August. Improved adaptive Gaussian mixture model for background subtraction. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. (Vol. 2, pp. 28-31). IEEE.

[20]   Ganesh, V. and Kamarasan, M., 2020. Deep learning based long short term memory model for emotions with intensity level sentiment classification for twitter texts. Int. J. Adv. Sci. Technol.

[21]   https://github.com/cair/Fire-Detection-Image-Dataset

 

[22]   Pushpa, B. and Kamarasan, M., Video Summarization Based on Gaussian Mixture Model and Kernel Support Vector Machine for Forest Fire Detection, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019

Cite this Article as :
Style #
MLA Saeed M. Aljaberi, Ahmed N. Al-Masri. "Automated Deep Learning based Video Summarization Approach for Forest Fire Detection." Journal of Intelligent Systems and Internet of Things, Vol. 5, No. 2, 2021 ,PP. 54-61 (Doi   :  https://doi.org/10.54216/JISIoT.050201)
APA Saeed M. Aljaberi, Ahmed N. Al-Masri. (2021). Automated Deep Learning based Video Summarization Approach for Forest Fire Detection. Journal of Journal of Intelligent Systems and Internet of Things, 5 ( 2 ), 54-61 (Doi   :  https://doi.org/10.54216/JISIoT.050201)
Chicago Saeed M. Aljaberi, Ahmed N. Al-Masri. "Automated Deep Learning based Video Summarization Approach for Forest Fire Detection." Journal of Journal of Intelligent Systems and Internet of Things, 5 no. 2 (2021): 54-61 (Doi   :  https://doi.org/10.54216/JISIoT.050201)
Harvard Saeed M. Aljaberi, Ahmed N. Al-Masri. (2021). Automated Deep Learning based Video Summarization Approach for Forest Fire Detection. Journal of Journal of Intelligent Systems and Internet of Things, 5 ( 2 ), 54-61 (Doi   :  https://doi.org/10.54216/JISIoT.050201)
Vancouver Saeed M. Aljaberi, Ahmed N. Al-Masri. Automated Deep Learning based Video Summarization Approach for Forest Fire Detection. Journal of Journal of Intelligent Systems and Internet of Things, (2021); 5 ( 2 ): 54-61 (Doi   :  https://doi.org/10.54216/JISIoT.050201)
IEEE Saeed M. Aljaberi, Ahmed N. Al-Masri, Automated Deep Learning based Video Summarization Approach for Forest Fire Detection, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 5 , No. 2 , (2021) : 54-61 (Doi   :  https://doi.org/10.54216/JISIoT.050201)
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