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Surveillance Video Synopsis Techniques : A Review

Shefali Gandhi, Tushar Vallabhbhai Ratanpara

Abstract


This is the era of video surveillance, not just security. The arrival of inexpensive surveillance cameras and increasing demands of security has caused an explosive growth of surveillance videos, which are used by government or other organizations for prevention or investigation of crime. As browsing such lengthy videos is very time consuming, most of the videos are never watched and analyzed. The video synopsis is a technique to represent such lengthy videos in a condensed way by showing multiple activities simultaneously. The purpose of this paper is to explain development stages, methods, limitations of video synopsis technique and its application in the field of surveillance video analysis.

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References


Badal, T., Nain, N., and Ahmed, M. 2017. Surveillance video synopsis while preserving object motion structure and interaction. In In proceedings of international conference on computer vision and image processing. pp.197–207.

Bagheri, S. and Zheng, J. 2014. Temporal mapping of surveillance video. In 22nd Interna- tional Conference on Pattern Recognition. pp.4128–4133.

Benjamin, P., Markus, H., Daniel, W., and Gunther, H. 2010. Information-based adaptive fast forward for visual surveillance. Multimedia Tools and Applications.

Chen, Y., Chen, C., Huang, C., and Hung, Y. 2007. Efficient hierarchical method for background subtraction. Pattern Recognit. Vol.40.

Cho, T. S., Butman, M., Avidan, S., and Freeman, W. 2008. The patch transform and its applications to image editing. In IEEE conference on computer vision and pattern recognition. pp.1–8.

Chou, C., Lin, C., Chiang, T., Chen, H., and Lee, S. 2015. Event-based surveillance video synopsis using trajectory clustering. In IEEE international conference on multimedia expo workshop. pp.1–5.

Farbman, Z., Hoffer, G., Lipman, Y.and Cohen-or, D., and Lischinski, D. 2009. Coor- dinates for instant image cloning. In ACM transactions on graphics (TOG) - proceedings of ACM. pp.67.

Feng, S., Liao, S., Yuan, Z., and LI., S. Z. 2010. Online principal background selection for video synopsis. In International conference on pattern recognition. pp.17–20.

Gandhi, S. and Ratanpara, T. 2017. Object-based surveillance video synopsis using genetic algorithm. Applied Video Processing in Surveillance and Monitoring Systems.

Hampapur, A.and Brown, L., Connell, J., Ekin, A., Haas, N., Lu, M., Merkl, H.and Pankanti, S., Senior, A., Shu, C., and Tian, Y. 2005. Smart video surveillance. IEEE signal processing magazine.

Hao, L., Cao, J., and Li, C. 2013. Research of grabcut algorithm for single camera video synopsis. In IEEE fourth international conference on Intelligent control and information processing. pp.632–637.

Hassanpour, H., Sedighi, M., and Manashty, A. R. 2011. Video frames background mod- eling: Reviewing the techniques. Journal of Signal and Information Processing .

He, Y., Qu, Z., Gao, C., and Sang, N. 2017. Fast online video synopsis based on potential collision graph. IEEE Signal Processing Letters.

Hsia, C. and Chiang, J. 2013. A complexity reduction method for video synopsis system.

In International symposium on intelligent signal processing and communications systems. pp.163–168.

Huang, C., C. P. Y. D. C. H. and Huang, G. 2014. Maximum a posteriori probability estimation for online surveillance video synopsis. In IEEE transactions on circuits and systems for video technology. pp.1417–1419.

Jin, J., Li, F., Gan, Z., and Cui, Z. 2016. Online video synopsis method through simple tube projection strategy. In Wireless communications signal processing (wcsp), 2016 8th international conference. pp.1–5.

Kaewtrakulpong, P. and Bowden, R. 2001. An improved adaptive background mixture model for real-time tracking with shadow detection. Video-based surveillance systems.

Kang, H., Matsushita, Y., Tang, X., and Chen, X. 2006. Spacetime video montage. In IEEE computer society conference on computer vision and pattern recognition. pp.1331–1338.

Kansagara, R., Thakore, D., and Joshi, M. 2014. A study on video summarization tech- niques. International journal of innovative research in computer and communication engi- neering Vol. 2.

Li, K., Yan, B., Wang, W., and Gharavi, H. 2016. An effective video synopsis approach with seam carving. IEEE Signal Processing Letters.

Li, Y., Lee, S., Yeh, C., and Kuo, C. 2006. Techniques for movie content analysis and skimming: tutorial and overview on video abstraction techniques. IEEE Signal Processing Magazine.

Li, Y., Zhang, T., and Tretter, D. 2001. An overview of video abstraction techniques.

Technical Report HPL.

Lin, L., Lin, W., Xiao, W., and Huang, S. 2017. An optimized video synopsis algorithm and its distributed processing model. Soft computing .

Lu, M., Wang, Y., and Pan, G. 2013. Generating fluent tubes in video synopsis. In IEEE international conference on acoustics, speech and signal processing. pp.2292–2296.

Nie, Y. and Xiao, C. 2013. Compact video synopsis via global spatiotemporal optimization.

In IEEE transactions on visualization and computer graphics. pp.1664–1676.

Parekh, H. S., Thakore, D. G., and Jaliya, U. K. 2014. A survey on object detection and tracking methods. International Journal of Innovative Research in Computer and Communication Engineering Vol.2.

Pe’rez, P., Gangnet, M., and Blake, A. 2003. Poisson image editing. In ACM Trans.

Graphics (TOG). pp.313–318.

Pritch, Y., Rav-acha, A., Gutman, A., and Peleg, S. 2007. Webcam synopsis: Peeking around the world. In IEEE 11th international conference on computer vision. pp.1–8.

Pritch, Y., Rav-acha, A., and Peleg, S. 2008. Nonchronological video synopsis and indexing.

In IEEE transactions on pattern analysis and machine intelligence. pp.1971–1984.

Rav-acha, A., Pritch, Y., and Peleg, S. 2006. Making a long video short: Dynamic video synopsis. In Proc. IEEE conf. computer vision and pattern recognition. pp.435–441.

Shah, R. and Ratanpara, T. 2017. An automatic content based video ranking from surveillance videos. International journal of engineering and technology Vol.9.

Sun, J., Zhang, W., Tang, X., and Shum, H. 2006. Background cut. In Proc. European Conference Computer Vision (ECCV 06). pp.628–641.

Tanaka, M., Kamio, R., and Okutomi, M. 2012. Seamless image cloning by a closed form solution of a modified poisson problem. SIGGRAPH Asia 2012 .

Truong, B. and Venkatesh, S. 2007. Video abstraction: A systematic review, classification.

In ACM transactions on multimedial computing, communications and applications. pp.3–3.

Yao, T., Xiao, M., Ma, C., Shen, C., and Li, P. 2014. Object based video synopsis,. In IEEE workshop on advanced research and technology in industry applications. pp.1138–1148.

Yogameena, B. and Sindhu, P. 2015. Synoptic video based human crowd behavior analysis for forensic video surveillance. In IEEE eight international conference on advances in pattern recognition. pp.1–6.

Zhang, Y., Lin, W., Zhang, G., Luo, C., Jiang, D., and Yao, C. 2014. A new approach for extracting and summarizing abnormal activities in surveillance videos. In IEEE Inter- national Conference on Multimedia and Expo Workshops (ICMEW). pp.1–5.

Zhong, R., Hu, R., and Wang, Z. 2014. Fast synopsis for moving objects using compressed video. IEEE signal processing letters Vol.21.

Zhu, B., Liu, W., and Yuan, G. 2014. A method for video synopsis based on multiple object tracking. In 5th IEEE international conference on software engineering and service science. pp.414–418.

Zhu, J. and Liao, S. 2015. Multi-camera joint video synopsis. IEEE transactions on circuits and systems for video technology Vol.PP.