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A time-slice optimization based weak feature association algorithm for video condensation

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Abstract

There are a lot of complex environments in real scene, such as illumination variation, shadow variation, object occlusion, which will directly affect the performance of video synopsis. In this paper, we adopt Grid Background Model as object detection algorithm, proposing algorithm based on weak feature to solve the object occlusion problem, at last we propose to use time-slice optimization algorithm to solve the visualization problem of video condensation. Specifically, Grid Background Model is adopted to segment the foreground from the background, then we use current frame to update background frame, and then binarize the foreground frame to perform Neighborhood illumination invariant shadow elimination. A clear foreground can be obtained by doing the procedure above as well as Gaussian noise elimination and morphological operation such as inflation and corrosion to remove cavities. Meanwhile, the outline of the object is extracted by using the canny edge detector. In the object tracking section, we will introduce how to use the weak features, such as color, speed and direction on the basis of location prediction based on tracking algorithm to perform object association, and the extraction of accurate information of abstract and outline of the object at the same time. Finally, in the video condensation section, we will describe how to use optical time-slice based minimum energy model to perform video condensation according to frame sequence. The experimental result shows that, the method mentioned above can provide a new approach for solving the occlusion problems of video condensation, and have better visualization of abstract video, and achieve up to 6 times concentration to the original video.

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References

  1. Chen Z, Huang W, Lv Z (2015) Towards a face recognition method based on uncorrelated discriminant sparse preserving projection. Multimedia Tools and Applications, pp 1–15

  2. Dedeoglu Y (2004) Moving object detection, tracking and classification for smart video surveillance. Ph.D. thesis, Bilkent University

  3. Eshel R, Moses Y (2010) Tracking in a dense crowd using multiple cameras. Int J Comput Vis 88(1):129–143

    Article  Google Scholar 

  4. Fu C, Zhang P, Jiang J, Yang K, Lv Z (2015) A bayesian approach for sleep and wake classification based on dynamic time warping method. Multimedia Tools and Applications, pp 1–20

  5. Fu Y, Guo Y, Zhu Y, Liu F, Song C, Zhou ZH (2010) Multi-view video summarization. IEEE Trans Multimedia 12(7):717–729

    Article  Google Scholar 

  6. Gu W, Lv Z, Hao M (2015) Change detection method for remote sensing images based on an improved markov random field. Multimedia Tools and Applications 2016

  7. Hanjalic A, Kakes G, Lagendijk RL, Biemond J (2001) Dancers: Delft advanced news retrieval system. In: Photonics west 2001-electronic imaging. International Society for Optics and Photonics, pp 301–310

  8. Jiang D, Xu Z, Lv Z (2015) A multicast delivery approach with minimum energy consumption for wireless multi-hop networks. Telecommun Syst 1–12

  9. Jiang D, Ying X, Han Y, Lv Z (2015) Collaborative multi-hop routing in cognitive wireless networks. Wirel Pers Commun 1–23

  10. Kim EY, Park SH (2006) Automatic video segmentation using genetic algorithms. Pattern Recogn Lett 27(11):1252–1265

    Article  Google Scholar 

  11. Leo CD, Manjunath BS (2014) Multicamera video summarization and anomaly detection from activity motifs. ACM Trans Sensor Netw (TOSN) 10(2):27

    Google Scholar 

  12. Lienhart R, Pfeiffer S, Effelsberg W (1997) Video abstracting. Commun ACM 40(12):54–62

    Article  Google Scholar 

  13. Lin Y, Yang J, Lv Z, Wei W, Song H (2015) A self-assessment stereo capture model applicable to the internet of things. Sensors 15(8):20,925–20,944

    Article  Google Scholar 

  14. Lv Z, Halawani A, Fen S, Li H et al (2015) Touch-less interactive augmented reality game on vision based wearable device. arXiv:1504.06359

  15. Lv Z, Halawani A, Feng S, Li H, Réhman SU (2014) Multimodal hand and foot gesture interaction for handheld devices. ACM Trans Multimed Comput Commun Appl (TOMM) 11(1s):10

    Google Scholar 

  16. Lv Z, Tek A, Da Silva F, Empereur-Mot C, Chavent M, Baaden M (2013) Game on, science-how video game technology may help biologists tackle visualization challenges. PloS One 8(3):57,990

    Article  Google Scholar 

  17. Park D, Byun H (2013) A unified approach to background adaptation and initialization in public scenes. Pattern Recogn 46(7):1985–1997

    Article  Google Scholar 

  18. Pritch Y, Ratovitch S, Hende A, Peleg S (2009) Clustered synopsis of surveillance video. In: 6th IEEE international conference on advanced video and signal based surveillance, 2009. AVSS’09. IEEE, pp 195–200

  19. Rav-Acha A, Pritch Y, Peleg S (2006) Making a long video short: dynamic video synopsis. In: IEEE computer society conference on computer vision and pattern recognition, 2006, vol 1. IEEE, pp 435–441

  20. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE computer society conference on computer vision and pattern recognition, 1999, vol 2. IEEE

  21. Su T, Wang W, Lv Z, Wu W, Li X (2016) Rapid delaunay triangulation for randomly distributed point cloud data using adaptive hilbert curve. Comput Graph 54:65–74

    Article  Google Scholar 

  22. Sundaram H (2002) Segmentation, structure detection and summarization of multimedia sequences. Ph.D. thesis, Columbia University

  23. Truong BT, Venkatesh S (2007) Video abstraction: a systematic review and classification. ACM Trans Multimed Comput Commun Appl (TOMM) 3(1):3

    Article  Google Scholar 

  24. Wang K, Zhou X, Li T, Zhao D, Lang M, Raicu I (2014) Optimizing load balancing and data-locality with data-aware scheduling. In: IEEE international conference on big data (Big Data), 2014. IEEE, pp 119–128

  25. Yang J, He S, Lin Y, Lv Z (2015) Multimedia cloud transmission and storage system based on internet of things. Multimedia Tools and Applications 2016

  26. Yin T, Han Y, Chen Y, Chen G, et al. (2011) Webvr web virtual reality engine based on p2p network. J Netw 6(7):990–998

    Google Scholar 

  27. Yoshitaka A, Sawada K (2012) Personalized video summarization based on behavior of viewer. In: 8th international conference on signal image technology and internet based systems (SITIS), 2012. IEEE, pp 661–667

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Acknowledgments

This study was supported by the National Natural Science Foundation of China(Grant no. U1504602), Postdoctoral Science Foundation of China (2015M572141).The authors wish to thank the Science and Technology Plan Projects of Henan Province for contract 162102310147, 132300410485 and 142300410463, the Key Research Projects of Universities in Henan Province for contract 15A520035 and 15A520124, under which the present work was possible.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhoukou Normal University, Zhoukou, Henan, 466001, China

    Yongfeng Cui, Wei Liu & Shi Dong

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, Hubei, Wuhan, China

    Shi Dong

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  1. Yongfeng Cui
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  2. Wei Liu
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  3. Shi Dong
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Correspondence to Yongfeng Cui.

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Cui, Y., Liu, W. & Dong, S. A time-slice optimization based weak feature association algorithm for video condensation. Multimed Tools Appl 75, 17515–17530 (2016). https://doi.org/10.1007/s11042-016-3473-4

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  • DOI: https://doi.org/10.1007/s11042-016-3473-4

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