Multimedia Tools and Applications

, Volume 75, Issue 24, pp 17187–17213 | Cite as

Video surveillance system based on a scalable application-oriented architecture

  • Amal Ben Hamida
  • Mohamed Koubaa
  • Henri Nicolas
  • Chokri Ben Amar
Article
First Online:
Received:
Revised:
Accepted:

Abstract

Nowadays, colossal numbers of facilities all over the world are protected from various types of threats by video surveillance cameras. Video surveillance systems are in urgent and increasing demand to ensure people, things and places security. These networks represent a huge amount of video to be transmitted, viewed and analyzed. However, current surveillance methodologies become increasingly ineffective as the number of cameras grows. To remedy this issue, we propose appending a pre-analysis stage to the video surveillance system. This stage extracts, compresses and sends only the information of interest from the captured video data. Through this paper, scalability, which has always been treated as a processing tool in video surveillance, is handled otherwise to generate more efficient and adaptable video surveillance systems. The pre-analysis phase works in a scalable way. It narrows down the analyzed data by extracting just the useful information depending on the final desired application. The originality of this work lies in the combination of the pre-analysis and the scalability to generate a progressive scalable video surveillance architecture responding to the end user needed application. The principal contributions mentioned in this paper are: i) A framework for a scalable application-oriented architecture for video surveillance systems; ii) A region of interest simplification method aiming to filter in a spatio-temporal way the captured data; iii) A region of interest modeling technique which simplifies the moving objects’ representation in the image plane. The evaluation step shows promising results and demonstrates the effectiveness of the suggested architecture.

Keywords

Video surveillance Scalability Video pre-analysis Spatio-temporal filtering Moving object modeling 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Alayed H, Frangoudes F, Neuman C (2013) Behavioral-based cheating detection in online first person shooters using machine learning techniques. In: IEEE Conference on Computational Intelligence in Games (CIG),pp 1–8Google Scholar
  2. 2.
    Amato A, Lecce VD (2011) Semantic classification of human behaviors in video surveillance systems. WSEAS Trans Comput 10(10):343–352Google Scholar
  3. 3.
    Antonio C, David F-L, Antonio SM, Juan JP, María LD (2013) Abandoned object detection on controlled scenes using kinect. Nat Artif Comput Eng Med Appl 7931:169–178Google Scholar
  4. 4.
    Bai YW, Xie ZL, Li ZH (2011) Design and implementation of a home embedded surveillance system with ultra-low alert power. IEEE Trans Consum Electron 57:153–159CrossRefGoogle Scholar
  5. 5.
    Barga D, Dalton MT (2014) A survey on moving object tracking in video. Int J Inf Theory 3:31–46CrossRefGoogle Scholar
  6. 6.
    Bektuzun E, Kucuksoz YS, Elif KM (2013) Real time tracking and detection of unusual circumstances of elderly people with RGB-d camera. In: Signal Processing and Communications Applications Conference (SIU), pp 1–5Google Scholar
  7. 7.
    Ben Hamida A, Koubaa M, Nicolas H, Ben Amar C (2013) Spatio-temporal video filtering for video surveillance applications. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW),pp 1–6Google Scholar
  8. 8.
    Ben Hamida A, Koubaa M, Nicolas H, Ben Amar C (2013) Video pre-analyzing and coding in the context of video surveillance applications. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW),pp 1–4Google Scholar
  9. 9.
    Ben Hamida A, Koubaa M, Nicolas H, Ben Amar C (2014) Parallelepipedic shape modeling for moving objects in video surveillance systems. In: IEEE Science and Information Conference (SAI). IEEE, pp 379–383Google Scholar
  10. 10.
    Ben Hamida A, Koubaa M, Nicolas H, Ben Amar C (2014) Toward scalable application-oriented video surveillance systems. In: IEEE Science and Information Conference (SAI). IEEE, pp 384–388Google Scholar
  11. 11.
    Boulay B, Bremond F, Thonnat M (2006) Applying 3D human model in a posture recognition system. Pattern Recognit Lett Spec Issue visCrime Detect Prev 27(15):1788–1796CrossRefGoogle Scholar
  12. 12.
    Brulin M, Nicolas H, Maillet C (2012) Analyse d’un trafic routier dans un contexte de vidéo surveillance. In: Proceedings of Sciences of Electronic, Technologies of Information and Telecommunications (SETIT)Google Scholar
  13. 13.
    Chamasemani FF, Affendey LS (2013) Systematic review and classification on video surveillance systems. Int J Inf Technol Comput Sci 5(7):87–102Google Scholar
  14. 14.
    Chambon S, Crouzil A (2002) Évaluation et comparaison de mesures de corrélationrobustes aux occultations. Rapport de recherche 2002-34-R, IRIT, Université PaulSabatier, Toulouse, France, DecemberGoogle Scholar
  15. 15.
    Chen T, Haussecker H, Bovyrin A, Belenov R, Rodyushkin K, Kuranov A, Eruhimov V (2005) Computer vision workload analysis: case study of video surveillance systems. Intell Technol J 9(2)Google Scholar
  16. 16.
    Chua J-L, Chang YC, Lim WK (2015) A simple vision-based fall detection technique for indoor video surveillance. SIViP 9(3):623–633CrossRefGoogle Scholar
  17. 17.
    Chundi M, Jianbin X, Wei Y, Tong L, Peiqin L (2015) A fast recognition algorithm for suspicious behavior in high definition videos. Multimed SystGoogle Scholar
  18. 18.
    Comaniciu D, Ramesh V, Andmeer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–575CrossRefGoogle Scholar
  19. 19.
    Coppi D, Calderara S, Cucchiara R (2011) Iterative active querying for surveillance data retrieval in crime detection and forensics. In: 4th International Conference on Imaging for Crime Detection and Prevention (ICDP), pp 1–6Google Scholar
  20. 20.
    Cucchiara R, Grana C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 25:1337–1342CrossRefGoogle Scholar
  21. 21.
    Cucchiara R, Prati A,Vezzani R (2005) Posture classification in a multi camera indoor environment. In: Proceedings of IEEE International Conference on Image Processing (ICIP), pp 725–728Google Scholar
  22. 22.
    Di CG, Soraghan JJ (2012) Robust complete occlusion handling in adaptive template matching target tracking. Electron Lett 48(14):831–832CrossRefGoogle Scholar
  23. 23.
    Dimitropoulos K, Semertzidis T, Grammalidis N (2009) Video and signal based surveillance for airport applications. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, pp 170–175Google Scholar
  24. 24.
    Duman D, Akar GB (2013) Moving vehicle classification. In : Conference on Signal Processing and Communications Applications (SIU), pp 1–4Google Scholar
  25. 25.
    Ezzahout A, Thami ROH (2013) Conception and development of a video surveillance system for detecting, tracking and profile analysis of a person. In: 3rd International Symposium for Knowledge Organization (ISKO), pp 1–5Google Scholar
  26. 26.
    Grois D, Kaminsky E, Hadar O (2010) ROI adaptive scalable video coding for limited bandwidth wireless networks. In: Wireless Days (WD), IFIP, pp 1–5Google Scholar
  27. 27.
    Grzegorz S, Piotr D (2014) Detection of vehicles stopping in restricted zones in video from surveillance cameras. Multimed Commun, Serv Secur 429:242–253CrossRefGoogle Scholar
  28. 28.
    Héctor F, Gómez A, Rafael MT, Susana AT, Antonio FC, Sylvie R, Alexandra GE, Patricia LG (2015) Identification of loitering human behaviour in video surveillance environments. In: International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC), pp 516–525Google Scholar
  29. 29.
    Hua-Tsung C, Li-Wu T, Hui-Zhen G, Suh-Yin L, Lin BSP (2012) Traffic Congestion Classification for Nighttime Surveillance Videos. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, pp 169–174Google Scholar
  30. 30.
    Imran S, Mubarak (2013) Multiframe many–many point correspondence for vehicle tracking in high density wide area aerial videos. Int J Comput Vis 104(2):198–219CrossRefMATHGoogle Scholar
  31. 31.
    In SK, Hong SC, Kwang MY, Jin YC, Seong GK (2010) Intelligent visual surveillance - a survey. Int J Control Autom Syst 8(5):926–939CrossRefGoogle Scholar
  32. 32.
    Julfa T, Bailing Z (2014) Simultaneous object tracking and classification for traffic surveillance. In: International Conference on Computer Science and Information Technology (CSAIT) pp 749–755Google Scholar
  33. 33.
    Jun L, Jinqiao W, Huazhong X, Hanqing L (2015) A real-time people counting approach in indoor environment. Multimed Model 8935:214–223Google Scholar
  34. 34.
    Ko T (2008) A survey on behavior analysis in video surveillance for homeland security applications. In: 37th Workshop on Applied Imagery Pattern Recognition (AIPR). IEEE, pp 1–8Google Scholar
  35. 35.
    Kruger M, Ziegler L, Heller K (2012) A generic Bayesian Network for identification and assessment of objects in maritime surveillance. In: 15th International Conference on Information Fusion (FUSION), pp 2309–2316Google Scholar
  36. 36.
    Lai CL, Yang JC, Chen YH (2007) A real time video processing based surveillance system for early fire and flood detection. In: Instrumentation and Measurement Technology Conference Proceedings (IMTC). IEEE, pp 1–6Google Scholar
  37. 37.
    Lambert P, De Schrijver D, Van Deursen D, De Neve W, Dhondt Y, Van de Walle R (2006) A real-time content adaptation framework for exploiting ROI scalability in H.264/AVC. In: Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems. Springer-Verlag, pp 442–453Google Scholar
  38. 38.
    Le A, Bir B, Songfan Y (2014) Unified face representation for individual recognition in surveillance videos. Wide Area Surveill 6:123–136CrossRefGoogle Scholar
  39. 39.
    Le LV, Dongbin Z, Zhijiang F (2014) Cheating behavior detection based-on pictorial structure model. In: 33rd Chinese Control Conference (CCC), pp 7274–7279Google Scholar
  40. 40.
    Le A, Kafai M, Bhanu B (2012) Face recognition in multi-camera surveillance videos using Dynamic Bayesian Network. In: Sixth International Conference on Distributed Smart Cameras (ICDSC), pp1-6Google Scholar
  41. 41.
    Lei W, Fuhai L (2012) The design of real-time monitoring system for enterprises in complex environments. In: International Conference on Systems and Informatics (ICSAI), pp 306–314Google Scholar
  42. 42.
    Liyan Z, Dmitri VK, Sharad M, Ronen V (2012) Context-based person identification framework for smart video surveillance. Mach Vis Appl 25(7):1711–1725Google Scholar
  43. 43.
    Loomans MJH, Koeleman JC (2011) Low-complexity wavelet-based scalable image & video coding for home-use surveillance. IEEE Trans Consum Electron 57:507–15CrossRefGoogle Scholar
  44. 44.
    Luo X, Wu Y, Huang Y, Zhang J (2011) Vehicle flow detection in real-time airborne traffic surveillance system. Trans Inst Meas Control 33:880–897CrossRefGoogle Scholar
  45. 45.
    Maddalena L, Petrosino A (2013) Stopped object detection by learning foreground model in videos. IEEE Transactions on Neural Networks and Learning Systems(24)5:723–735Google Scholar
  46. 46.
    Mukherjee S, Saha B, Jamal I, Leclerc R, Ray N (2011) A novel framework for automatic passenger counting. In: 18th IEEE International Conference on Image Processing (ICIP). IEEE, pp 2969–2972Google Scholar
  47. 47.
    Negri P (2014) Estimating the queue length at street intersections by using a movement feature space approach. IET Image Process 8(7):406–416MathSciNetCrossRefGoogle Scholar
  48. 48.
    Panahi R, Gholampour I, Jamzad M (2013) Real time occlusion handling using Kalman Filter and mean-shift. In: 8th Iranian Conference on Machine Vision and Image Processing (MVIP), pp 320–323Google Scholar
  49. 49.
    Peng Z, Yanning Z, Tony T, Sabu E (2014) Moving people tracking with detection by latent semantic analysis for visual surveillance applications. Multimed Tools Appl 68(3):991–1021CrossRefGoogle Scholar
  50. 50.
    Prati A, Mikic I, Trivedi M, Cucchiara R (2003) Detecting moving shadows: formulation, algorithms and evaluation. IEEE Trans Pattern Anal Mach Intell 25:918–924CrossRefGoogle Scholar
  51. 51.
    Ruiyue X, Yepeng G, Yizhen H (2015) Multiple human detection and tracking based on head detection for real-time video surveillance. Multimed Tools Appl 74(3):729–742CrossRefGoogle Scholar
  52. 52.
    Salvi G (2014) An automated nighttime vehicle counting and detection system for traffic surveillance. In: International Conference on Computational Science and Computational Intelligence (CSCI), pp 131–136Google Scholar
  53. 53.
    Sanghyuk P, Yoo CD (2012) Video scene analysis and irregular behavior detection for intelligent surveillance system. In: 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp 577–581Google Scholar
  54. 54.
    Scotti G, Cuocolo A, Coelho C, Marchesotti L (2005) A novel pedestrian classification algorithm for a high definition dual camera 360 degrees surveillance system. In: Proceedings of the International Conference on Image Processing (ICIP), pp 880–883Google Scholar
  55. 55.
    Sehchan O, Sunghyuk P, Changmu L (2007) A platform surveillance monitoring system using image processing for passenger safety in railway station. In: International Conference on Control, Automation and Systems, ICCAS, pp 394–398Google Scholar
  56. 56.
    Setitra I, Larabi S (2014) Background Subtraction Algorithms with Post-processing: A Review. In: 22nd International Conference on Pattern Recognition (ICPR), pp 2436–2441Google Scholar
  57. 57.
    Simone C, Lucio M, Pietro M, Carlo R (2014) Event based switched dynamic bayesian networks for autonomous cognitive crowd monitoring. Wide Area Surveillance : pp 93–122Google Scholar
  58. 58.
    Sohn H, AnzaKu E, De Neve W, Ro YM, Plataniotis K (2009) Privacy protection in video surveillance systems using scalable video coding. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, pp 424–429Google Scholar
  59. 59.
    Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 246–252Google Scholar
  60. 60.
    Sung CL, Ram N (2014) Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system. Mach Vis Appl 25(1):133–143CrossRefGoogle Scholar
  61. 61.
    Szpak ZL, Tapamo JR (2011) Maritime surveillance: tracking ships inside a dynamic background using a fast level-set. Expert Syst Appl 38(6):6669–66680CrossRefGoogle Scholar
  62. 62.
    Wang S, Chen L, Zhou Z, Sun X, Dong J (2015) Human fall detection in surveillance video based on PCANet. Multimed Tools Appl:1–11Google Scholar
  63. 63.
    Wedi T (2003) Motion compensation in H.264/AVC. IEEE Trans Circ Syst Video Technol 13:577–586CrossRefGoogle Scholar
  64. 64.
    Xinfeng B, Javanbakhti S, Zinger S, Wijnhoven R (2014) Context-based object-of-interest detection for a generic traffic surveillance analysis system. In: 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, pp 136–141Google Scholar
  65. 65.
    Yaning W, Hong Z (2015) Pedestrian detection and counting based on ellipse fitting and object motion continuity for video data analysis. Intell Comput Theor Methodologies 9225:378–387CrossRefGoogle Scholar
  66. 66.
    Yingkun X, Lei Q, Guorong L, Qingming H (2013) An efficient occlusion detection method to improve object trackers. In: 20th IEEE International Conference on Image Processing (ICIP), pp 2445–2449. doi: 10.1109/ICIP.2013.6738504
  67. 67.
    Yoneyama A, Yeh C, Kuo CC (2005) Robust vehicle and traffic information extraction for highway surveillance. EURASIP J Appl Signal Process 2005:2305–2321CrossRefMATHGoogle Scholar
  68. 68.
    Yun K, Jeong H, Yi KM, Kim SW, Choi JY (2014) Motion interaction field for accident detection in traffic surveillance video. In: 22nd International Conference on Pattern Recognition (ICPR), pp 3062–3067Google Scholar
  69. 69.
    Yunbo R (2015) Automatic vehicle recognition in multiple cameras for video surveillance. Vis Comput 31(3):271–280CrossRefGoogle Scholar
  70. 70.
    Zang Q, Klette R (2003) Object classification and tracking in video surveillance. Computer Analysis of Images and Patterns, Springer Berlin Heidelberg (2756), Springer-Verlag:198–205Google Scholar
  71. 71.
    Zebin C, Zhu LY, Hao L, Ke Z (2014) Counting people in crowded scenes by video analyzing. In: IEEE 9th Conference on Industrial Electronics and Applications (ICIEA). IEEE, pp 1841–1845Google Scholar
  72. 72.
    Zezhi C, Pears N, Freeman M, Austin J (2014) A Gaussian mixture model and support vector machine approach to vehicle type and color classification. IET Intell Transp Syst 8(2):135–144CrossRefGoogle Scholar
  73. 73.
    Ziliani F (2005) The importance of ‘scalability’ in video surveillance architectures. In: The IEE International Symposium on Imaging for Crime Detection and Prevention (ICDP). IEEE, pp 29–32Google Scholar
  74. 74.
    Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. In: International Conference on Pattern Recognition (ICPR), pp 28–31Google Scholar
  75. 75.
    Zivkovic Z, van der Heijden F (2006) Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn Lett 27:773–780CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Amal Ben Hamida
    • 1
    • 2
  • Mohamed Koubaa
    • 1
  • Henri Nicolas
    • 2
  • Chokri Ben Amar
    • 1
  1. 1.REGIM: REsearch Groups on Intelligent Machines, ENIS: National Engineering School of SfaxUniversity of SfaxSfaxTunisia
  2. 2.LaBRI: Laboratoire Bordelais de Recherche en InformatiqueUniversity of BordeauxTalenceFrance

Personalised recommendations