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2018 | OriginalPaper | Buchkapitel

V-LESS: A Video from Linear Event Summaries

verfasst von : Krishan Kumar, Deepti D. Shrimankar, Navjot Singh

Erschienen in: Proceedings of 2nd International Conference on Computer Vision & Image Processing

Verlag: Springer Singapore

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Abstract

In this paper, we propose a novel V-LESS technique for generating the event summaries from monocular videos. We employed Linear Discriminant Analysis (LDA) as a machine learning approach. First, we analyze the features of the frames, after breaking the video into the frames. Then these frames are used as input to the model which classifies the frames into active frames and inactive frames using LDA. The clusters are formed with the remaining active frames. Finally, the events are obtained using the key-frames with the assumption that a key-frame is either the centroid or the nearest frame to the centroid of an event. The users can easily opt the number of key-frames without incurring the additional computational overhead. Experimental results on two benchmark datasets show that our model outperforms the state-of-the-art models on Precision and F-measure. It also successfully abates the video content while holding the interesting information as events. The computational complexity indicates that the V-LESS model meets the requirements for the real-time applications.

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Literatur
1.
Zurück zum Zitat Singh N., et al., “A Novel Position Prior Using Fusion of Rule of Thirds and Image Center for Salient Object Detection,” MTAP (2016), pp. 1–18. Singh N., et al., “A Novel Position Prior Using Fusion of Rule of Thirds and Image Center for Salient Object Detection,” MTAP (2016), pp. 1–18.
2.
Zurück zum Zitat Vermaak J., et al., “Rapid summarization and browsing of video sequences,” BMVC, (2002), pp. 1–10. Vermaak J., et al., “Rapid summarization and browsing of video sequences,” BMVC, (2002), pp. 1–10.
3.
Zurück zum Zitat Krishan K., et al., “Event BAGGING: A novel event summarization approach in multi-view surveillance videos,” IEEE IESC’17. Krishan K., et al., “Event BAGGING: A novel event summarization approach in multi-view surveillance videos,” IEEE IESC’17.
4.
Zurück zum Zitat Truong B.T., Venkatesh S., “Video abstraction: a systematic review and classification,” ACM Trans. Multimed. Comp. Comm. App., 3, 1, (2007), 37 pages. Truong B.T., Venkatesh S., “Video abstraction: a systematic review and classification,” ACM Trans. Multimed. Comp. Comm. App., 3, 1, (2007), 37 pages.
5.
Zurück zum Zitat Chowdhury A. S., et al., “Video Storyboard Design using Delaunay Graphs,” Int. Conf. on Pattern Recog., (2012), pp. 3108–3111. Chowdhury A. S., et al., “Video Storyboard Design using Delaunay Graphs,” Int. Conf. on Pattern Recog., (2012), pp. 3108–3111.
6.
Zurück zum Zitat Mundur P., Rao Y., Yesha Y., “Keyframe-based video summarization using Delaunay clustering,” Int. J. Digit. Libr., 6, 2, (2006), pp. 219–232. Mundur P., Rao Y., Yesha Y., “Keyframe-based video summarization using Delaunay clustering,” Int. J. Digit. Libr., 6, 2, (2006), pp. 219–232.
7.
Zurück zum Zitat Kumar K., et al., “Equal Partition based Clustering approach for Event Summarization in Videos,” SITIS, 2016, pp. 119–126. Kumar K., et al., “Equal Partition based Clustering approach for Event Summarization in Videos,” SITIS, 2016, pp. 119–126.
8.
Zurück zum Zitat Chang H.S., et al., “Efficient video indexing scheme for content-based retrieval,” IEEE TCSVT, 9, 8, (1999), pp. 1269–1279. Chang H.S., et al., “Efficient video indexing scheme for content-based retrieval,” IEEE TCSVT, 9, 8, (1999), pp. 1269–1279.
9.
Zurück zum Zitat Gong Y., Liu X., “Video summarization using singular value decomposition,” IEEE CVPR, 2, (2000), pp. 174–180. Gong Y., Liu X., “Video summarization using singular value decomposition,” IEEE CVPR, 2, (2000), pp. 174–180.
10.
Zurück zum Zitat K. Kumar, et al., “Eratosthenes sieve based key-frame extraction technique for event summarization in videos,” MTAP (2017), pp. 1–22. K. Kumar, et al., “Eratosthenes sieve based key-frame extraction technique for event summarization in videos,” MTAP (2017), pp. 1–22.
11.
Zurück zum Zitat Zhuang Y., et al., “Adaptive key frame extraction using unsupervised clustering,” IEEE ICIP, 1, (1998), pp. 866–870. Zhuang Y., et al., “Adaptive key frame extraction using unsupervised clustering,” IEEE ICIP, 1, (1998), pp. 866–870.
12.
Zurück zum Zitat K. Kumar, et al., “SOMES: An efficient SOM technique for Event Summarization in multi-view surveillance videos,” Springer ICACNI’17. K. Kumar, et al., “SOMES: An efficient SOM technique for Event Summarization in multi-view surveillance videos,” Springer ICACNI’17.
13.
Zurück zum Zitat Sahouria, E., et al., “Content analysis of video using principal components.” IEEE TCSVT, 9, 8, (1999), pp. 1290–1298. Sahouria, E., et al., “Content analysis of video using principal components.” IEEE TCSVT, 9, 8, (1999), pp. 1290–1298.
14.
Zurück zum Zitat Altman, E. I., et al. “Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience).”, Journal of banking & finance, 18, 3, (1994), pp. 505–529. Altman, E. I., et al. “Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience).”, Journal of banking & finance, 18, 3, (1994), pp. 505–529.
15.
Zurück zum Zitat Furini M., et al., “Stimo: still and moving video storyboard for the web scenario,” Multimed. Tools Appl. 46, 1, (2010), pp. 47–69. Furini M., et al., “Stimo: still and moving video storyboard for the web scenario,” Multimed. Tools Appl. 46, 1, (2010), pp. 47–69.
16.
Zurück zum Zitat Avila S., et al., “Vsumm: a mechanism designed to produce static video summaries and a novel evaluation method,” Pattern Recognit. Lett., 32, 1, (2011), pp. 56–68. Avila S., et al., “Vsumm: a mechanism designed to produce static video summaries and a novel evaluation method,” Pattern Recognit. Lett., 32, 1, (2011), pp. 56–68.
18.
Zurück zum Zitat Cong Y., et al., “Towards scalable summarization of consumer videos via sparse dictionary selection,” IEEE TMM, 14, 1, (2012), pp. 66–75. Cong Y., et al., “Towards scalable summarization of consumer videos via sparse dictionary selection,” IEEE TMM, 14, 1, (2012), pp. 66–75.
19.
Zurück zum Zitat Guan G., et al., “Keypoint based keyframe selection,” IEEE Trans. Circuits Syst. Video Tech., 23, 4, (2013), pp. 729–734. Guan G., et al., “Keypoint based keyframe selection,” IEEE Trans. Circuits Syst. Video Tech., 23, 4, (2013), pp. 729–734.
20.
Zurück zum Zitat Mei S., et al., “Video summarization via minimum sparse reconstruction,” Pattern Recog., 48, 2, (2015), pp. 522–533. Mei S., et al., “Video summarization via minimum sparse reconstruction,” Pattern Recog., 48, 2, (2015), pp. 522–533.
Metadaten
Titel
V-LESS: A Video from Linear Event Summaries
verfasst von
Krishan Kumar
Deepti D. Shrimankar
Navjot Singh
Copyright-Jahr
2018
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-7895-8_30