Big data analytics for video surveillance

Abstract

This article addresses the usage and scope of Big Data Analytics in video surveillance and its potential application areas. The current age of technology provides the users, ample opportunity to generate data at every instant of time. Thus in general, a tremendous amount of data is generated every instant throughout the world. Among them, amount of video data generated is having a major share. Education, healthcare, tours and travels, food and culture, geographical exploration, agriculture, safety and security, entertainment etc., are the key areas where a tremendous amount of video data is generated every day. A major share among it are taken by the daily used surveillance data captured from the security purpose camera and are recorded everyday. Storage, retrieval, processing, and analysis of such gigantic data require some specific platform. Big Data Analytics is such a platform, which eases this analysis task. The aim of this article is to investigate the current trends in video surveillance and its applications using Big Data Analytics. It also aims to focus on the research opportunities for visual surveillance in Big Data frameworks. We have reported here the state-of-the-art surveillance schemes for four different imaging modalities: conventional video scene, remotely sensed video, medical diagnostics, and underwater surveillance. Several works were reported in this research field over recent years and are categorized based on the challenges solved by the researchers. A list of tools used for video surveillance using Big Data framework is presented. Finally, research gaps in this domain are discussed.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Subudhi BN, Nanda PK, Ghosh A (2011) A change information based fast algorithm for video object detection and tracking. IEEE Trans on Cir and Syst for Vid Tech 21(7):993–1004

    Google Scholar 

  2. 2.

    Kwon O, Lee N, Shin B (2014) Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. of Info. Man. 34(3):387–394

    Google Scholar 

  3. 3.

    Yadav C, Wang S, Kumar M (2013) Algorithm and approaches to handle large data- a survey. Int J of Comp Sci and Net 2(3):1–5

    Google Scholar 

  4. 4.

    Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J of Info Man 35:137–144

    Google Scholar 

  5. 5.

    Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proc of the VLDB Endowment 5(12):2032–2033

    Google Scholar 

  6. 6.

    Fan J, Han F, Liu H (2014) Challenges of big data analysis. Nat Sci Rev 1(2):293–314

    Google Scholar 

  7. 7.

    Cohen J, Dolan B, Dunlap M, Hellerstein JM, Welton C (2009) Mad skills: new analysis practices for big data. Very large databases conf, pp 1–6

    Google Scholar 

  8. 8.

    Whitepaper: Cisco VNI forecast and methodology, 2015-2020, http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.html. Accessed 18 Jan 2018

  9. 9.

    Lin J, Ryaboy D (2014) Scaling big data mining infrastructure: the twitter experience. SIGKDD Explorations 14(2):6–19

    Google Scholar 

  10. 10.

    Dean, J (2014) Big data, data mining, and machine learning. Wiley

  11. 11.

    Herodotou H, Lim H, Luo G, Borisov N, Dong L, Cetin FB, Babu S (2011) Starfish: a self-tuning system for big data analytics. 5th biennial Conf. On Inno. Data. Syst Res:261–272

  12. 12.

    Ghosh A, Subudhi BN, Ghosh S (2012) Object detection from videos captured by moving camera by fuzzy edge incorporated Markov random field and local histogram matching. IEEE Trans. on Cir. and Syst. for Vid. Tech. 22(8):1127–1135

    Google Scholar 

  13. 13.

    Hu W, Xie N, Li L, Zeng X, Maybank S (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans on Syst Man, and Cyb, Part C: Appl and Rev 41(6):797–819

    Google Scholar 

  14. 14.

    Zhan B, Monekosso D, Remagnino P, Velastin S, Xu L-Q (2008) Crowd analysis: a survey. Mac Vis and Appl 19(5–6):345–357

    Google Scholar 

  15. 15.

    Subudhi BN, Nanda PK, Ghosh A (2011) Entropy based region selection for moving object detection. Patt Recog Lett 32(15):2097–2108

    Google Scholar 

  16. 16.

    Pouyanfar S, Yang Y, Chen SC, Shyu ML, Iyengar SS (2018) Multimedia big data analytics: a survey. ACM Comp. Sur. 51(1):10.1–10.34

    Google Scholar 

  17. 17.

    Heidemann KMJ, Probst F (2012) Online social networks: a survey of a global phenomenon. Comput Netw 56(18):3866–3878

    Google Scholar 

  18. 18.

    Ezaki, N., Bulacu, M., Schomaker, L (2004) Text detection from natural scene images: towards a system for visually impaired persons. 17th Int. Conf. On Patt. Recog., 2, 683–686

  19. 19.

    Saracoglu A, Alatan AA (2006) Automatic video text localization and recognition. IEEE 14th Sig Proc and Com Appl:1–4

  20. 20.

    Lin W, Jia S, Yang F, Takase K (2004) Topological navigation of mobile robot using ID tag and WEB camera. Int Conf on Intel Mech and Auto:644–649

  21. 21.

    Ayed AB, Halima MB, Alimi AM (2015) MapReduce based text detection in big data natural scene videos. Procedia Comp Sci 53:216–223

    Google Scholar 

  22. 22.

    Opitz M, Diem M, Fiel S, Kleber F, Sablatnig R (2014) End-to-end text recognition using local ternary patterns, MSER and deep convolutional nets, 11th IAPR Int. Wor on Doc Ana Sys:186–190

  23. 23.

    Turki H, Ben Halima M, Alimi AM (2017) Text detection based on MSER and CNN features, 14th IAPR Int. Conf. On doc. Ana. And Recog., 949–954

  24. 24.

    Selmi Z, Ben Halima M, Alimi AM (2017) Deep learning system for automatic license plate detection and recognition, 14th IAPR Int. Conf. On doc. Ana. And Recog., 1132–1138

  25. 25.

    Shivakumara P, Tang D, Asadzadehkaljahi M, Lu T, Pal U, Hossein Anisi M (2018) CNN-RNN based method for license plate recognition. CAAI Trans on Intel Tech 3(3):169–175

    Google Scholar 

  26. 26.

    Vincent N, Ogier JM (2019) Shall deep learning be the mandatory future of document analysis problems? Pat Recog 86:281–289

    Google Scholar 

  27. 27.

    Babar M, Arif F, Jan MA, Tan Z, Khan F (2019) Urban data management system: towards big data analytics for internet of things based smart urban environment using customized Hadoop. Fut. Gen. Comp. Sys. 96:398–409

    Google Scholar 

  28. 28.

    Ko T (2008) A survey on behavior analysis in video surveillance for homeland security applications. 37th IEEE App Im Pat Rec Work:1–8

  29. 29.

    Cristani M, Raghavendra R, Bue AD, Murino V (2013) Human behavior analysis in video surveillance: a social signal processing perspective. Neurocomputing. 100:86–97

    Google Scholar 

  30. 30.

    Guo S, Luo H, Yong L (2015) A big data-based workers behavior observation in China metro construction. Procedia Eng 123:190–197

    Google Scholar 

  31. 31.

    Zitouni MS, Dias J, Al-Mualla M, Bhaskar H (2015) Hierarchical crowd detection and representation for big data analytics in visual surveillance. IEEE Int Conf on Syst, Man, and Cyb:1827–1832

  32. 32.

    Gao Z, Zhang H, Xu GP, Xue YB, Hauptmann AG (2015) Multi-view discriminative and structured dictionary learning with group sparsity for human action recognition. Sig Pro 112:83–97

    Google Scholar 

  33. 33.

    Pan Z, Liu S, Fu W (2017) A review of visual moving target tracking. Multi. Tools and App. 76(16):16989–17018

    Google Scholar 

  34. 34.

    Shao Z, Cai J, Wang Z (2018) Smart monitoring cameras driven intelligent processing to big surveillance video data. IEEE Trans on Big Data 4(1):105–116

    Google Scholar 

  35. 35.

    Liu G, Liu S, Muhammad K, Sangaiah AK, Doctor F (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access 6:29283–29296

    Google Scholar 

  36. 36.

    Gao Z, Han TT, Zhu L, Zhang H, Wang Y (2018) Exploring the cross-domain action recognition problem by deep feature learning and cross-domain learning. IEEE Access 6:68989–69008

    Google Scholar 

  37. 37.

    Ray KS, Chakraborty S (2019) Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background. J of Vis Com and Im Rep 58:662–674

    Google Scholar 

  38. 38.

    Jansohn C, Ulges A, Breuel TM (2009) Detecting pornographic video content by combining image features with motion information. In: 17th ACM Int. Conf. On multimedia, pp 601–604

    Google Scholar 

  39. 39.

    Behrad A, Salehpour M, Ghaderian M, Saiedi M, Nasrollah Barati M (2012) Content-based obscene video recognition by combining 3D spatiotemporal and motion-based features. EURASIP J on Image and Vid Proc 23:1–17

    Google Scholar 

  40. 40.

    Zhu T, Phipps D, Pridgen A, Crandall JR, Wallach DS (2013) The velocity of censorship: high-fidelity detection of microblog post deletions. 22nd USENIX Conf. On security, 227–240

  41. 41.

    Cheng X, Mehrdad F, Ma X, Zhang C, Liu J (2014) Understanding the YouTube partners and their data: measurement and analysis. China Com 11(12):26–34

    Google Scholar 

  42. 42.

    Wu J, Zhang Z, Hong Y, Wen Y (2015) Cloud radio access network (C-RAN): a primer. IEEE Netw 29(1):35–41

    Google Scholar 

  43. 43.

    Sheng M, Han W, Huang C, Li J, Cui S (2015) Video delivery in heterogenous crans: architectures and strategies. IEEE Wireless Com 22(3):14–21

    Google Scholar 

  44. 44.

    Ruiz M, Germán M, Contreras LM, Velasco L (2016) Big data-backed video distribution in the telecom cloud. Comp Com 84:1–11

    Google Scholar 

  45. 45.

    Chen CLP, Zhang C-Y (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Info. Sci. 275:314–347

    Google Scholar 

  46. 46.

    Leeson A, Pablo A, Ghosh S (2014) Understanding how big data and crowd movements will shape the cities of tomorrow. Euro Trans Conf:1–12

  47. 47.

    Zhang X, Yu Q, Yu H (2018) Physics inspired methods for crowd video surveillance and analysis: a survey. IEEE Access 6:66816–66830

    Google Scholar 

  48. 48.

    Kajo I, Kamel N, Malik AS (2018) An adaptive block-based matching algorithm for crowd motion sequences. Multi Tools and App 77(2):1783–1809

    Google Scholar 

  49. 49.

    Huang S, Li X, Zhang Z, Wu F, Gao S, Ji R, Han J (2018) Body structure aware deep crowd counting. IEEE Trans. on Im. Pro. 27(3):1049–1059

    MathSciNet  MATH  Google Scholar 

  50. 50.

    Shami M, Maqbool S, Sajid H, Ayaz Y, Cheung SCS (2018) People counting in dense crowd images using sparse head detections. IEEE trans. On Cir. And sys. For vid. In: Tech

    Google Scholar 

  51. 51.

    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comp Vis and Im Und 110(3):346–359

    Google Scholar 

  52. 52.

    Ravanbakhsh M, Nabi M, Mousavi H, Sangineto E, Sebe N (2018) March) Plug-and-play cnn for crowd motion analysis: an application in abnormal event detection. IEEE Win Conf on App of Com Vis:1689–1698

  53. 53.

    Li Y (2018) A deep spatiotemporal perspective for understanding crowd behavior. IEEE Trans on Multi 20(12):3289–3297

    Google Scholar 

  54. 54.

    Mandal B, Fajtl J, Argyriou V, Monekosso D, Remagnino P (2018) Deep residual network with subclass discriminant analysis for crowd behavior recognition. 25th IEEE Int. Conf. On Im. Pro., 938–942

  55. 55.

    Fleuret F, Berclaz J, Lengagne R, Fua P (2008) Multicamera people tracking with a probabilistic occupancy map. IEEE Trans on Patt Anal and Mach Intel 30(2):267–282

    Google Scholar 

  56. 56.

    Murtaza T, Cavallaro A (2011) Distributed and decentralized multicamera tracking. IEEE Sig Proc Magazine 28(3):46–58

    Google Scholar 

  57. 57.

    Gundecha P, Liu H (2012) Mining social media: a brief introduction. Tutorials in Operations Research 1(4):1–17

    Google Scholar 

  58. 58.

    Chen Z, Liao W, Xu B, Liu H, Li Q, Li H, Xiao C, Zhang H, Li Y, Bao W, Yang D (2015) Object tracking over a multiple-camera network. IEEE Int Conf on Multi Big Data:276–279

  59. 59.

    Blat J, Evans A, Kim H, Imre E, Polok L, Ila V, Nikolaidis N, Zemc’ık P, Tefas A, Smrzˇ P, Hilton A, Pitas I (2016) Big data analysis for media production. Proc. of the IEEE. 104(11):2085–2113

    Google Scholar 

  60. 60.

    Richards JA, Jia X (2006) Remote sensing digital image analysis: an introduction. Springer-Verlag, Berlin

    Google Scholar 

  61. 61.

    Campbell JB, Wynne RH (2011) Introduction to remote sensing. The Guilford Press, New York

    Google Scholar 

  62. 62.

    Lenhart D, Hinz S, Leitloff J, Stilla U (2008) Automatic traffic monitoring based on aerial image sequences. Patt Recog and Image Anal 18:400–405

    Google Scholar 

  63. 63.

    Carrano C (2009) Ultra-scale vehicle tracking in low spatial resolution and low frame-rate overhead video. Proc. of SPIE. 7445, LLNL-CONF-413376

  64. 64.

    Presnar M, Raisanen A, Pogorzala D, Kerekes J, Rice A (2010) Dynamic scene generation, multimodal sensor design, and target tracking demonstration for hyperspectral/polarimetric performance-driven sensing. Proc of SPIE 7672:76720T

    Google Scholar 

  65. 65.

    Palaniappan K, Bunyak F, Kumar P, Ersoy I, Jaeger S, Ganguli K, Haridas A, Fraser J, Rao R, Seetharaman G (2010) Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video. 13th Conf. On info. FUSION (FUSION), 1–8

  66. 66.

    Xiao J, Cheng H, Sawhney H, Han F (2010) Vehicle detection and tracking in wide field-of-view aerial video. IEEE Conf on Comp Vis and Patt Recog (CVPR) 679–684(2010)

  67. 67.

    Palaniappan K, Rao R, Seetharaman G (2011) Wide-area persistent airborne video: architecture and challenges. Distributed video sensor networks. B. Bhanu et al. Springer, London, pp 349–371

    Google Scholar 

  68. 68.

    Skyland N (2012) Big data: what is NASA doing with big data today. Open. Gov. open-access article

  69. 69.

    Meng L, Kerekes JP (2012) Object tracking using high resolution satellite imagery. IEEE J. of Sel. Top. in Appl. Earth Observ. and Remote Sens. 5(1):146–152

    Google Scholar 

  70. 70.

    Oliveira SF, Furlinger K, Kranzlmuller D (2012) Trends in computation, communication and storage and the consequences for data-intensive science. IEEE 14th Int. Conf. On high Perfor. Comp. And com. & IEEE 9th Int. Conf. On embed. Software and Syst. (HPCC-ICESS), 572–579

  71. 71.

    Bhattacharyya A (1943) On a measure of divergence between two statistical populations defined by their probability distributions. Bull of the Cal Math Soc 35:99–109

    MathSciNet  MATH  Google Scholar 

  72. 72.

    Subudhi BN, Bovolo F, Ghosh A, Bruzzone L (2014) Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images. Opt & Las Tech 57:284–292

    Google Scholar 

  73. 73.

    Rathore MMU, Paul A, Ahmad A, Chen BW, Huang B, Ji W (2015) Real-time big data analytical architecture for remote sensing application. IEEE J. of Sel. Top. in Appl. Earth Observ. and Remote Sens. 8(10):4610–4621

    Google Scholar 

  74. 74.

    Cavallaro G, Riedel M, Richerzhagen M, Benediktsson JA, Plaza A (2015) On understanding big data impacts in remotely sensed image classification using support vector machine methods. IEEE J of Sel Top in Appl Earth Observ and Remote Sens 8(10):4634–4646

    Google Scholar 

  75. 75.

    Chi M, Plaza A, Benediktsson JA, Sun Z, Shen J, Zhu Y (2016) Big data for remote sensing: challenges and opportunities. Proc of the IEEE 104(11):2207–2219

    Google Scholar 

  76. 76.

    Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. ACM Com 51(1):107–113

    Google Scholar 

  77. 77.

    Xiang W, Wang G, Pickering M, Zhang Y (2016) Big video data for light-field-based 3D telemedicine. IEEE Netw 30(3):30–38

    Google Scholar 

  78. 78.

    Luo J, Wu M, Gopukumar D, Zhao Y (2016) Big data application in biomedical research and health care: a literature review. Biomed Infor Insights 8(1–10)

  79. 79.

    Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C (2016) Big data for infectious disease surveillance and modeling. The J of Infect Diseases 214(4):S375–S379

    Google Scholar 

  80. 80.

    Rangayyan RM (2004) Biomedical image analysis. CRC Press

  81. 81.

    Eberhardt R, Anantham D, Ernst A, Feller-Kopman D, Herth F (2007) Multimodality bronchoscopic diagnosis of peripheral lung lesions. Am J of Resp and Critical Care Med 176(1):36–41

    Google Scholar 

  82. 82.

    Suinesiaputra A, Brett C, Pau M-G, Abram Y (2015) Big heart data: advancing health informatics through data sharing in cardiovascular imaging. IEEE J of Biomed and Health Infor 19(4):1283–1290

    Google Scholar 

  83. 83.

    Belle, A., Thiagarajan, R., Reza Soroushmehr, S. M., Navidi, F., Beard, D. A., Najarian, K.: Big data analytics in healthcare. Biomed Res Int 2015(370194), 1–16 (2015).

  84. 84.

    Menze BH, Bjoern H, Leemput KV, Lashkari D, Weber M, Ayache N, Golland P (2010) A generative model for brain tumor segmentation in multi-modal images. Med Image Comp and Comp-Assist Inter–MICCAI:151–159

  85. 85.

    Young AA, Alejandro FF (2009) Computational cardiac atlases: from patient to population and back. Exp Physio 94(5):578–596

    Google Scholar 

  86. 86.

    Manolis AJ, Eftichia C, Ioanna Z (2015) Modern diagnostic approach for the assessment of cardiac damage in hypertension: 3D, CT and MRI. Ass of Preclin Org Dam in Hyp:25–37

  87. 87.

    Liu J, Zhang Z, Wong DW, Xu Y, Yin F, Cheng J, Tan NM (2013) Automatic glaucoma diagnosis through medical imaging informatics. J of the American Med Infor Asso 20(6):1021–1027

    Google Scholar 

  88. 88.

    Vallieres M, Freeman C, Skamene S, Issam El N (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys in Med and Bio 60(14):5471–5496

    Google Scholar 

  89. 89.

    Lee CH, Yoon H (2017) Medical big data: promise and challenges. Kidney Res and Clin Pract 36(1):3–11

    Google Scholar 

  90. 90.

    Fang R, Pouyanfar S, Yang Y, Chen SC, Iyengar SS (2016) Computational health informatics in the big data age: a survey. ACM Comp Sur 49(1):12.1–12.36

    Google Scholar 

  91. 91.

    Meggitt DJ, Roderick DK, Cooke KP (1999) Advanced technologies for undersea surveillance of modern threats: riding the crest into the 21st century. OCEANS '99 MTS/IEEE 1:289–294

    Google Scholar 

  92. 92.

    Minami M, Agbanhan J, Asakura T (1999) Manipulator visual servoing and tracking of fish using genetic algorithm. Ind Rob 26(4):278–289

    Google Scholar 

  93. 93.

    Foresti GL, Gentili S (2000) A vision based system for object detection in underwater images. Int J of Patt Recog and Art Intel 14(2):167–188

    Google Scholar 

  94. 94.

    Sehgal A, Kadarusman J, Fife LD (2004) TOUCH: a robotic vision system for underwater object tracking. IEEE Conf on Robo, Auto and Mech 1:455–460

    Google Scholar 

  95. 95.

    Chuang MC, Hwang JN, Ye JH, Huang SC, Williams K (2017) Underwater fish tracking for moving cameras based on deformable multiple kernels. IEEE Trans on Syst, Man, and Cyb: Syst 7(9):2467–2477

    Google Scholar 

  96. 96.

    Rout DK, Bhat PG, Veerakumar T, Subudhi BN, Chaudhury S (2017) A novel five-frame difference scheme for local change detection in underwater video. 4th IEEE Int. Conf. On Im. Info. Proc., 1–6

  97. 97.

    Mondal A, Ghosh S, Ghosh A (2017) Partially camouflaged object tracking using modified probabilistic neural network and fuzzy energy based active contour. Int J of Comp Vis 122(1):116–148

    MathSciNet  Google Scholar 

  98. 98.

    Rout DK, Subudhi BN, Veerakumar T, Chaudhury S (2018) Spatio-contextual Gaussian mixture model for local change detection in underwater video. Exp Sys With Appl 97:117–136

    Google Scholar 

  99. 99.

    Palazzo S, Spampinato C, Giordano D (2014) Large scale data processing in ecology: a case study on long-term underwater video monitoring. 22nd Euromicro Int. Conf. On Paral., Distri., and net.-based proc., Torino, 312–316

  100. 100.

    Alharbi A, Reda AA, Hesham A, Sanguthevar R, Jun H (2014) Efficient pipeline architectures for underwater big data analytic. IEEE Int Sym on Sig Proc and Info Tech pp:161–166

  101. 101.

    Lebart K, Smith C, Trucco E, Lane DM (2003) Automatic indexing of underwater survey video: algorithm and benchmarking method. IEEE J. of Ocean. Eng. 28(4):673–686

    Google Scholar 

  102. 102.

    Trucco E, Plakas K (2006) Video tracking: a concise survey. IEEE J of Ocean Eng 31(2):520–529

    Google Scholar 

  103. 103.

    Xiang X, Yu C, Lapierre L, Zhang J, Zhang Q (2018) Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles. Int J Fuzzy Syst 20:572–586

    MathSciNet  Google Scholar 

  104. 104.

    Hewlett Packard Enterprise website, Big Data solutions, available from: https://www.hpe.com/us/en/solutions/big-data.html. Accessed 1 Mar 2019

  105. 105.

    Louridas P, Ebert C (2013) Embedded analytics and statistics for big data. IEEE Softw 30(6):33–39

    Google Scholar 

  106. 106.

    Abaker I, Hashem T, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of “big data” on cloud computing: review and open research issues. Info Syst 47:98–115

    Google Scholar 

  107. 107.

    Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J. of Paral. and Distri. Comp. 74(7):2561–2573

    Google Scholar 

  108. 108.

    Assunção MD, Calheiros RN, Bianchi S, Netto MAS, Buyya R (2015) Big data computing and clouds: trends and future directions. J of Paral and Distri Comp 79–80:3–15

    Google Scholar 

  109. 109.

    Khan S, Shakil KA, Alam M (2017) Big data computing using cloud-based technologies: challenges and future perspectives, networks of the future: architectures, technologies, and implementations, editors: Mahmoud Elkhodr, Qusay Hassan, Seyed Shahrestani, Chapman and Hall/CRC

  110. 110.

    Kehoe B, Patil S, Abbeel P, Goldberg K (2015) A survey of research on cloud robotics and automation. IEEE Trans on Auto Sci and Eng 12(2):398–409

    Google Scholar 

  111. 111.

    Pan Z, Liu S, Sangaiah AK, Muhammad K (2018) Visual attention feature (VAF): a novel strategy for visual tracking based on cloud platform in intelligent surveillance systems. J of Par and Dist Comp 120:182–194

    Google Scholar 

  112. 112.

    Lua software tool available at: https://www.lua.org/. Accessed 1 Mar 2019

  113. 113.

    Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar SS (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comp. Sur. 51(5):92.1–92.36

    Google Scholar 

  114. 114.

    Sindagi VA, Patel VM (2018) A survey of recent advances in cnn-based single image crowd counting and density estimation. Pat Rec Let 107:3–16

    Google Scholar 

  115. 115.

    Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Info Fus 42:146–157

    Google Scholar 

  116. 116.

    Pradhan R, Aygun RS, Maskey M, Ramachandran R, Cecil DJ (2018) Tropical cyclone intensity estimation using a deep convolutional neural network. IEEE Trans on Im Pro 27(2):692–702

    MathSciNet  MATH  Google Scholar 

  117. 117.

    Helbing D (2019) Societal, economic, ethical and legal challenges of the digital revolution: from big data to deep learning, artificial intelligence, and manipulative technologies. Tow Dig Enl 47-72

  118. 118.

    Ahmad J, Muhammad K, Lloret J, Baik SW (2018) Efficient conversion of deep features to compact binary codes using Fourier decomposition for multimedia big data. IEEE Trans on Ind Info 14(7):3205–3215

    Google Scholar 

  119. 119.

    Li B, Han X, Wu D (2018) Real-time crowd density estimation based on convolutional neural networks. Int Conf on Intel Trans, Big Data & Smart City:690–694

  120. 120.

    Shamsolmoali P, Zareapoor M, Jain DK, Jain VK, Yang J (2018) Deep convolution network for surveillance records super-resolution. Multi. Tools and App.:1–15

  121. 121.

    Xie S, Zhang X, Cai J (2018) Video crowd detection and abnormal behavior model detection based on machine learning method. Neu Comp and App:1–10

  122. 122.

    Xu Y, Lu L, Xu Z, He J, Zhou J, Zhang C (2018) Dual-channel CNN for efficient abnormal behavior identification through crowd feature engineering. Mac Vis and App:1–14

  123. 123.

    Ghosh A (2016) Big Data and its Utility Consulting Ahead 10(1):52–69

    Google Scholar 

  124. 124.

    Verma JP, Agrawal S, Patel B, Patel A (2016) Big data analytics: challenges and applications for text, audio, video, and social media data. Int J on Soft Comp, Art Intel and Appl 5(1):41–51

    Google Scholar 

  125. 125.

    Ghosh A, Seiffert U, Jain L (2007) Evolutionary computation in bioinformatics. J of Intel and Fuzzy Syst 18(7):25–26

    Google Scholar 

  126. 126.

    Ma Y, Wu H, Wang L, Huang B, Ranjan R, Zomaya A, Jie W (2015) Remote sensing big data computing: challenges and opportunities. Fut Gen Comp Sys 51:47–60

    Google Scholar 

  127. 127.

    Li Y, Wang S, Tian Q, Ding X (2015) A survey of recent advances in visual feature detection. Neurocomputing 149:736–751

    Google Scholar 

  128. 128.

    Ali HH, Moftah HM, Youssif AA (2018) Depth-based human activity recognition: a comparative perspective study on feature extraction. Fut Comp and Info J 3(1):51–67

    Google Scholar 

  129. 129.

    Alcantarilla PF, Bartoli A, Davison AJ (2012) KAZE features. Euro Conf on Comp Vis:214–227

  130. 130.

    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int. J. of Comp. Vis. 60(2):91–110

    Google Scholar 

  131. 131.

    Hore S, Chatterjee S, Chakraborty S, Shaw RK (2018) Analysis of different feature description algorithm in object recognition. Comp. Vis.: con., meth., tools, and Appl., IGI Global, 601–635

  132. 132.

    Sadeghian A, Alahi A, Savarese S (2017) Tracking the untrackable: learning to track multiple cues with long-term dependencies. IEEE Int Conf on Comp Vis:300–311

  133. 133.

    Li P, Wang D, Wang L, Lu H (2018) Deep visual tracking: review and experimental comparison. Pat. Recog. 76:323–338

    Google Scholar 

  134. 134.

    Subudhi BN, Ghosh S, Ghosh A (2015) Application of Gibbs–Markov random field and Hopfield-type neural networks for detecting moving objects from video sequences captured by static camera. Soft Comp 19(10):2769–2781

    Google Scholar 

  135. 135.

    Ghosh A, Chakraborty D, Law A (2018) Artificial intelligence in internet of things. CAAI Trans. on Intel. Tech. 3(4):208–218

    Google Scholar 

  136. 136.

    Chakraborty D, Narayanan V, Ghosh A (2019) Integration of deep feature extraction and ensemble learning for outlier detection. Pat. Recog. 89:161–171

    Google Scholar 

  137. 137.

    Subudhi BN, Ghosh S, Shiu SC, Ghosh A (2016) Statistical feature bag based background subtraction for local change detection. Info Sci 366:31–47

    MathSciNet  Google Scholar 

  138. 138.

    Subudhi BN, Ghosh S, Cho SB, Ghosh A (2016) Integration of fuzzy Markov random field and local information for separation of moving objects and shadows. Info. Sci. 331:15–31

    MathSciNet  Google Scholar 

  139. 139.

    Subudhi BN, Ghosh S, Nanda PK, Ghosh A (2017) Moving object detection using spatio-temporal multilayer compound Markov random field and histogram thresholding based change detection. Multi Tools and Appl 76(11):13511–13543

    Google Scholar 

  140. 140.

    Subudhi BN, Ghosh S, Ghosh A (2017) Moving object detection using multi-layer Markov random field model. Pat Recog and Big Data:687–711

  141. 141.

    Dubuisson S, Gonzales C (2016) A survey of datasets for visual tracking. Mac. Vis. and Appl. 27(1):23–52

    Google Scholar 

  142. 142.

    Multiple object tracking benchmark: http://motchallenge.net. Accessed 2 Mar 2019

  143. 143.

    MILtrack dataset: https://bbabenko.github.io/miltrack.html. Accessed 28 May 2019

  144. 144.

    CAVIAR test case scenarios: http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/. Accessed 2 Mar 2019

  145. 145.

    TRECVID homepage: http://www-nlpir.nist.gov/projects/trecvid/. Accessed 2 Mar 2019

  146. 146.

    UCSD pedestrian database (2009) http://www.svcl.ucsd.edu/projects/peoplecnt/index.htm. Accessed 2 Mar 2019

  147. 147.

    CDNET dataset: http://changedetection.net/. Accessed 2 Mar 2019

  148. 148.

    VOT Challenge dataset: http://www.votchallenge.net/. Accessed 2 Mar 2019

  149. 149.

    UWCD dataset: http://underwaterchangedetection.eu/. Accessed 2 Mar 2019

  150. 150.

    F4K dataset: http://f4k.dieei.unict.it/datasets/bkg_modeling/. Accessed 2 Mar 2019

  151. 151.

    ReefVid dataset: http://www.reefvid.org/. AccessNational Institute of Technology Goaed 2 Mar 2019

  152. 152.

    Lyon D (2014) Surveillance, Snowden, and Big Data: Capacities, consequences, critique. J of Big Data & Soc 1(2):1–12

    Google Scholar 

  153. 153.

    Ruhe MHO, Dalaff C, Kuhne RD (2003) Traffic monitoring and traffic flow measurement by remote sensing systems. IEEE Intel Transport Syst 1:760–764

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ashish Ghosh.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Subudhi, B.N., Rout, D.K. & Ghosh, A. Big data analytics for video surveillance. Multimed Tools Appl 78, 26129–26162 (2019). https://doi.org/10.1007/s11042-019-07793-w

Download citation

Keywords

  • Video surveillance
  • Big Data
  • Data Science
  • Big Data Analytics for video