Abstract
In the digital era, the growth of multimedia data is increasing at a rapid pace, which demands both effective and efficient summarization techniques. Such advanced techniques are required so that the users can quickly access the video content, recorded by multiple cameras for a certain period. At present, it is very challenging to manage and search a huge amount of multiview video data, which contains the inter-views dependencies, significant illumination changes, and many low-active frames. This work highlights an efficient summarization technique to summarize and then search the events in such multi-view videos over cloud through text query. Deep learning framework is employed to extract the features of moving objects in the frames. The inter-views dependencies among multiple views of the video are captured via local alignment. Parallel Virtual Machines (VMs) in the Cloud environment have been used to process the multiple video clip independently at a time. Object tracking is applied to filter the low-active frames. Experimental Results indicate that the model successfully reduces the video content, while preserving the momentous information in the form of the events. A computing analysis also indicates that it meets the requirement of real-time applications.
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References
Almeida J, Leite NJ, Torres RDS (2013) Online video summarization on compressed domain. J Vis Commun Image Represent 24(6):729–738
Ayguade E, Navarro JJ, Jimenez-Gonzalez D (2007) Smith-waterman algorithm, daring. Tersedia pada. [Online] available: http://docenciatextit.ac.upc.edu/master/AMPP/slides/ampp_sw_presentation.pdf
Chen K-W, Lai C-C, Lee P-J, Chen C-S, Hung Y-P (2011) Adaptive learning for target tracking and true linking discovering across multiple non-overlapping cameras. IEEE Trans Multimedia 13(4):625–638
Dumont E, Merialdo B (2009) Rushes video parsing using video sequence alignment. In: 2009 seventh international workshop on content-based multimedia indexing. IEEE, pp 44–49
Fu Y, Guo Y, Zhu Y, Liu F, Song C, Zhou Z-H (2010) Multi-view video summarization. IEEE Trans Multimed 12(7):717–729
Gygli M, Grabner H, Gool LV (2015) Video summarization by learning submodular mixtures of objectives. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3090–3098
Hao Y, Mu T, Goulermas JY, Jiang J, Hong R, Wang M (2017) Unsupervised t-distributed video hashing and its deep hashing extension. IEEE Trans Image Process 26(11):5531–5544
Hong R, Li L, Cai J, Tao D, Wang M, Tian Q (2017) Coherent semantic-visual indexing for large-scale image retrieval in the cloud. IEEE Trans Image Process 26(9):4128–4138
Jian M, Zhang W, Yu H, Cui C, Nie X, Zhang H, Yin Y (2018) Saliency detection based on directional patches extraction and principal local color contrast. J Vis Commun Image Represent 57:1–11
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Kuanar SK, Ranga KB, Chowdhury AS (2015) Multi-view video summarization using bipartite matching constrained optimum-path forest clustering. IEEE Trans Multimed 17(8):1166–1173
Kumar K, Kurhekar M (2016) Economically efficient virtualization over cloud using docker containers. In: 2016 IEEE international conference on cloud computing in emerging markets (CCEM). IEEE, pp 95–100
Kumar K, Kurhekar M (2017) Sentimentalizer: Docker container utility over Cloud. In: 2017 ninth international conference on advances in pattern recognition (ICAPR). IEEE, pp 1–6
Kumar K, Shrimankar (2017) F-DES: Fast and deep event summarization. IEEE Trans Multimed 20(2):323–334
Kumar K, Shrimankar DD (2018) Deep event learning boost-up approach: Delta. Multimed Tools Appl 77(20):26635–26655
Kumar K, Shrimankar DD (2018) ESUMM: Event summarization on scale-free networks. IETE Technical Review
Kumar K, Shrimankar DD, Singh N (2016) Equal partition based clustering approach for event summarization in videos. In: 2016 12th international conference on signal-image technology & internet-based systems (SITIS). IEEE, pp 119–126
Kumar K, Shrimankar DD, Singh N (2017) Event bagging: A novel event summarization approach in multiview surveillance videos. In: 2017 international conference on innovations in electronics, signal processing and communication (IESC). IEEE, pp 106–111
Kumar K, Shrimankar DD, Singh N (2018) Eratosthenes sieve based key-frame extraction technique for event summarization in videos. Multimedia Tools Appl 77(6):7383–7404
Kumar K, Shrimankar DD, Singh N (2018) SOMES: an efficient SOM technique for event summarization in multi-view surveillance videos. In: Recent findings in intelligent computing techniques. Springer, Singapore, pp 383–389
Kurzhals K, John M, Heimerl F, Kuznecov P, Weiskopf D (2016) Visual movie analytics. IEEE Trans Multimed 18(11):2149–2160
Liu J, Gong M, Qin AK, Tan KC (2019) Bipartite differential neural network for unsupervised image change detection. IEEE Trans Neural Netw Learn Sys 31(3):876–890
Michael G, John D (1991) Sequence analysis primer. Palgrave Macmillan, London, pp 169–175
Ou S-H, Lee C-H, Somayazulu VS, Chen Y-K, Chien S-Y (2014) On-line multi-view video summarization for wireless video sensor network. IEEE J Select Topics Sig Process 9(1):165–179
Panda R, Roy-Chowdhury AK (2017) Multi-view surveillance video summarization via joint embedding and sparse optimization. IEEE Trans Multimed 19 (9):2010–2021
Panda R, Mithun NC, Roy-Chowdhury AK (2017) Diversity-aware multi-video summarization. IEEE Trans Image Process 26(10):4712–4724
Potapov D, Douze M, Harchaoui Z, Schmid C (2014) Category-specific video summarization. In: European conference on computer vision. Springer, Cham, pp 540–555
Rav-Acha A, Pritch Y, Peleg S (2006) Making a long video short: Dynamic video synopsis. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 1. IEEE, pp 435–441
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252
Taherkhani A, Belatreche A, Li Y, Maguire LP (2018) A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks. IEEE Trans Neural Netw Learn Sys 29(11):5394–5407
Tong W, Zhong S (2016) A unified resource allocation framework for defending against pollution attacks in wireless network coding systems. IEEE Trans Inform Foren Sec 11(10):2255–2267
Wang C, Zhang H, Yang L, Cao X, Xiong H (2017) Multiple semantic matching on augmented N-partite graph for object co-segmentation. IEEE Trans Image Process 26 (12):5825–5839
Wong SC, Stamatescu V, Gatt A, Kearney D, Lee I, McDonnell MD (2017) Track everything: Limiting prior knowledge in online multi-object recognition. IEEE Trans Image Process 26(10):4669–4683
Xu B, Wang X, Jiang Y-G (2016) Fast summarization of user-generated videos: Exploiting semantic, emotional, and quality clues. IEEE MultiMedia 23 (3):23–33
Zhao B, Xing EP (2014) Quasi real-time summarization for consumer videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2513–2520
Zhao Z-Q, Zheng P, Xu S-T, Wu X (2019) Object detection with deep learning: A review. IEEE Trans Neural Netw Learn Sys 30(11):3212–3232
[Online] https://www.ctu.edu.vn/dvxe/Bioinformatic%20course/manuals/blast/blastmanual/fasta.htm. Retrieved (2016)
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Kumar, K. Text query based summarized event searching interface system using deep learning over cloud. Multimed Tools Appl 80, 11079–11094 (2021). https://doi.org/10.1007/s11042-020-10157-4
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DOI: https://doi.org/10.1007/s11042-020-10157-4