Abstract

The manual search through the surveillance video archives for a specific object or event is very time-consuming and tedious task due to the large volume of video data captured by many installed surveillance cameras. Therefore, the solution to accelerate and facilitate this process is to design an automatic video surveillance with the efficient and effective video indexing, video data model, query formulation and language, as well as visualization interface. There are many challenges, for developing a powerful query processing module, formulating complex queries and selecting suitable similarity matching strategy to detect any abnormality based on semantic content of the video using various query types. This study presents a novel video surveillance indexing and retrieval framework to cope with the above challenges. The proposed framework consists of three main modules i.e., pre-processing, query processing and retrieval processing. Moreover, it supports an efficient search and actively refines the retrieval result by formulating various query types including: query-by-text, query-by-example and query-by-region.

Keywords:

Data modeling, query formulation, query processing, video indexing, video surveillance, video surveillance retrieval,

References

1. Benabbas, Y., N. Ihaddadene and C. Djeraba. 2011. Motion pattern extraction and event detection for automatic visual surveillance. J. Image Video Process., Vol. 2011, Article No. 7.
   
2. Calderara, S., R. Cucchiara and A. Prati, 2006. Multimedia surveillance: Content-based retrieval with multicamera people tracking. Proceeding of the 4th ACM International Workshop on Video Surveillance and Sensor Networks (VSSN, 2006). Santa Barbara, California, USA, pp: 95-100.
   
3. Chamasemani, F.F. and L.S. Affendey, 2013. Systematic review and classification on video surveillance systems. Int. J. Inform. Technol. Comput. Sci., 5: 87.
   CrossRef    
4. Chiang, C.C. and H.F. Yang, 2015. Quick browsing and retrieval for surveillance videos. Multimed. Tools Appl., 74(9): 2861-2877.
   CrossRef    
5. Conte, D., P. Foggia, G. Percannella, F. Tufano and M. Vento, 2010. A method for counting moving people in video surveillance videos. EURASIP J. Adv. Sig. Pr., 2010: 231240.
   CrossRef    
6. Durak, N., A. Yazici and R. George, 2007. Online surveillance video archive system. In: Cham, T.J. et al. (Eds.), MMM 2007. LNCS 4351, Springer-Verlag, Berlin, Heidelberg, pp: 376-385.
   
7. Hampapur, A., L. Brown, J. Connell, A. Ekin, N. Haas et al., 2005. Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking. IEEE Signal Proc. Mag., 22(2): 38-51.
   CrossRef    
8. Hampapur, A., L. Brown, R. Feris, A. Senior, Chiao-Fe Shu et al., 2007. Searching surveillance video. Proceeding of IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS, 2007), pp: 75-80.
   
9. Hu, W., D. Xie, Z. Fu, W. Zeng and S. Maybank, 2007. Semantic-based surveillance video retrieval. IEEE T. Image Process., 16: 1168-1181.
   CrossRef    PMid:17405446    
10. Jiang, P. and X. Qin, 2010. Keyframe-based video summary using visual attention clues. IEEE MultiMedia, 17: 64-73.
    
11. Jung, Y.K., K.W. Lee and Y.S. Ho, 2001. Content-based event retrieval using semantic scene interpretation for automated traffic surveillance. IEEE T. Intell. Transp., 2: 151-163.
    CrossRef    
12. Kim, J.S., D.H. Yeom and Y.H. Joo, 2011. Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems. IEEE T. Consum. Electr., 57: 1165-1170.
    CrossRef    
13. Le, T.L., M. Thonnat, A. Boucher and F. Brémond, 2008. A query language combining object features and semantic events for surveillance video retrieval. In: Satoh, S., F. Nack and M. Etoh (Eds.), MMM 2008. LNCS 4903, Springer-Verlag, Berlin, Heidelberg, pp: 307-317.
    CrossRef    
14. Le, T.L., M. Thonnat, A. Boucher and F. Bremond, 2009. Surveillance video indexing and retrieval using object features and semantic events. Int. J. Pattern Recogn., 23: 1439-1476.
    CrossRef    
15. Le, T.L., A. Boucher, M. Thonnat and F. Brémond, 2010. Surveillance video retrieval: What we have already done? Proceeding of 3rd International Conference on Communications and Electronics (ICCE, 2010).
    
16. Lee, H., A. Smeaton, N. O'Connor and N. Murphy. 2005. User-interface to a CCTV video search system. Proceeding of the IEE International Symposium on Imaging for Crime Detection and Prevention (ICDP, 2005), pp: 39-43.
    CrossRef    
17. Lyons, D., T. Brodsky, E. Cohen-Solal and A. Elgammal, 2000. Video content analysis for surveillance applications. Proceeding of Philips Digital Video Technologies Workshop.
    
18. Nam, Y., S. Hong and S. Rho, 2013. Data modeling and query processing for distributed surveillance systems. New Rev. Hypermedia Multimedia, 19: 299-327.
    CrossRef    
19. Sabbar, W., A. Chergui and A. Bekkhoucha, 2012. Video summarization using shot segmentation and local motion estimation. Proceeding of the 2nd International Conference on Innovative Computing Technology (INTECH), pp: 190-193.
    CrossRef    
20. Saykol, E., U. Güdükbay and Ö. Ulusoy, 2005. A database model for querying visual surveillance videos by integrating semantic and low-level features. In: Candan, K.S. and A. Celentano (Eds.), MIS 2005. LNCS 3665, Springer-Verlag, Berlin, Heidelberg, pp: 163-176.
    CrossRef    
21. Saykol, E., U. Güdükbay and Ö. Ulusoy, 2010. Scenario-based query processing for video-surveillance archives. Eng. Appl. Artif. Intel., 23: 331-345.
    CrossRef    
22. Stringa, E. and C.S. Regazzoni, 1998. Content-based retrieval and real time detection from video sequences acquired by surveillance systems. Proceeding of International Conference on Image Processing (ICIP, 1998), 133: 138-142.
    CrossRef    
23. Stringa, E. and C.S. Regazzoni, 2000. Real-time video-shot detection for scene surveillance applications. IEEE T. Image Process., 9: 69-79.
    CrossRef    PMid:18255373    
24. Xu, Y. and D. Song, 2010. Systems and algorithms for autonomous and scalable crowd surveillance using robotic PTZ cameras assisted by a wide-angle camera. Auton. Robot., 29: 53-66.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.