Advertisement

Artificial Intelligence Review

, Volume 50, Issue 2, pp 283–339 | Cite as

Suspicious human activity recognition: a review

  • Rajesh Kumar Tripathi
  • Anand Singh Jalal
  • Subhash Chand Agrawal
Article

Abstract

Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.

Keywords

Abandoned object Theft detection Fall detection Accidents Violence Fire detection 

References

  1. Achkar F, Amer A (2007) Hysteresis-based selective gaussian mixture models for real- timebackground maintenance. SPIE Vis Commun Image Process 6508:J1–J11Google Scholar
  2. Adam A, Rivlin E, Shimshoni I, Reinitz D (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30(3):555–560CrossRefGoogle Scholar
  3. Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv (CSUR) 43(3):16CrossRefGoogle Scholar
  4. Aird B, Brown A (1997) Detection and alarming of the early appearance of fire using cctv cameras. In: Nuclear engineering internat. fire and safety conference, London, vol 24, p 26Google Scholar
  5. Akdemir U, Turaga P, Chellappa R (2008) An ontology based approach for activity recognition from video. In: Proceedings of the 16th ACM international conference on Multimedia, ACM, pp 709–712Google Scholar
  6. Aköoz Ö, Karsligil M (2010) Severity detection of traffic accidents at intersections based on vehicle motion analysis and multiphase linear regression. In: 13th International IEEE conference on intelligent transportation systems (ITSC), 2010, IEEE, pp 474–479Google Scholar
  7. Allgovision (2015) Advanced video analytics for traffic/parking management. http://www.allgovision.com/traffic-praking.php
  8. Amer A (2005) Voting-based simultaneous tracking of multiple video objects. IEEE Trans Circuit Syst Video Technol 15(11):1448–1462CrossRefGoogle Scholar
  9. Anderson D, Keller JM, Skubic M, Chen X, He Z (2006) Recognizing falls from silhouettes. In: 28th annual international conference of the IEEE engineering in medicine and biology society, 2006. EMBS’06. IEEE, pp 6388–6391Google Scholar
  10. Asodds (2011) An abandoned and stolen object discrimination dataset. http://wwwvpu.eps.uam.es/asodds
  11. Auvinet E, Multon F, Saint-Arnaud A, Rousseau J, Meunier J (2011) Fall detection with multiple cameras: an occlusion-resistant method based on 3-d silhouette vertical distribution. IEEE Trans Inf Technol Biomed 15(2):290–300CrossRefGoogle Scholar
  12. Auvinet E, Rougier C, Meunier J, St-Anaud A, Rousseau J (2010) Multiple Cameras Fall Data Set. DIRO-Universite de Montrial, Technical Report 1350Google Scholar
  13. Bangare PS, Uke NJ, Bangare SL (2012) Implementation of abandoned object detection in real time environment. Int J Comput Appl 57(12):13–16Google Scholar
  14. Beleznai C, Gemeiner P, Zinner C (2013) Reliable left luggage detection using stereo depth and intensity cues. In: IEEE international conference on computer vision workshops (ICCVW), 2013, IEEE, pp 59–66Google Scholar
  15. Benezeth Y, Jodoin PM, Saligrama V (2011) Abnormality detection using low-level co-occurring events. Pattern Recogn Lett 32(3):423–431CrossRefGoogle Scholar
  16. Bevilacqua A, Bevilacqua R (2002) Effective object segmentation in a traffic monitoring application. In: ICVGIP 2002 conference proceedings, Ahmedabad, India, CiteseerGoogle Scholar
  17. Bhargava M, Chen CC, Ryoo MS, Aggarwal JK (2009) Detection of object abandonment using temporal logic. Mach Vis Appl 20(5):271–281CrossRefGoogle Scholar
  18. Bird N, Atev S, Caramelli N, Martin R, Masoud O, Papanikolopoulos N (2006) Real time, online detection of abandoned objects in public areas. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006. ICRA 2006. IEEE, pp 3775–3780Google Scholar
  19. Borges PVK, Izquierdo E (2010) A probabilistic approach for vision-based fire detection in videos. IEEE Trans Circuit Syst Video Technol 20(5):721–731CrossRefGoogle Scholar
  20. Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev 11:31–66CrossRefzbMATHGoogle Scholar
  21. Brulin D, Benezeth Y, Courtial E (2012) Posture recognition based on fuzzy logic for home monitoring of the elderly. IEEE Trans Inf Technol Biomed 16(5):974–982CrossRefGoogle Scholar
  22. Candamo J, Shreve M, Goldgof DB, Sapper DB, Kasturi R (2010) Understanding transit scenes: a survey on human behavior-recognition algorithms. IEEE Trans Intell Transp Syst 11(1):206–224CrossRefGoogle Scholar
  23. Cappellini V, Mattii L, Mecocci A (1989) An intelligent system for automatic fire detection in forests. In: Third international conference on image processing and its applications, 1989, IET, pp 563–570Google Scholar
  24. Caviar fall on floor dataset (2004). http://homepages.inf.ed.ac.uk/rbf/caviardata1/
  25. Celik T, Ozkaramanli H, Demirel H (2007) Fire and smoke detection without sensors: image processing based approach. In: 15th European signal processing conference, EUSIPCO, pp 147–158Google Scholar
  26. Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
  27. Chen CC, Aggarwal J (2008) An adaptive background model initialization algorithm with objects moving at different depths. In: 15th IEEE international conference on image processing, 2008. ICIP 2008, IEEE, pp 2664–2667Google Scholar
  28. Chen TH, Wu PH, Chiou YC (2004) An early fire-detection method based on image processing. In: International conference on image processing, ICIP’04. 2004, IEEE, vol 3, pp 1707–1710Google Scholar
  29. Chen YT, Lin YC, Fang WH (2010) A hybrid human fall detection scheme. In: 17th IEEE international conference on image processing (ICIP), 2010, IEEE, pp 3485–3488Google Scholar
  30. Chien SY, Ma SY, Chen LG (2002) Efficient moving object segmentation algorithm using background registration technique. IEEE Trans Circuit Syst Video Technol 12(7):577–586CrossRefGoogle Scholar
  31. Chitra M, Geetha MK, Menaka L, et al (2013) Occlusion and abandoned object detection for surveillance applications. Int J Comput Appl Technol Res 2(6):708–metaGoogle Scholar
  32. Chua JL, Chang YC, Lim WK (2013) A simple vision-based fall detection technique for indoor video surveillance. SIViP 9(3):623–633CrossRefGoogle Scholar
  33. Chuang CH, Hsieh JW, Tsai LW, Ju PS, Fan KC, (2008) Suspicious object detection using fuzzy-color histogram. In: IEEE international symposium on circuits and systems, ISCAS 2008, IEEE, pp 3546–3549Google Scholar
  34. Chuang CH, Hsieh JW, Tsai LW, Chen SY, Fan KC (2009) Carried object detection using ratio histogram and its application to suspicious event analysis. IEEE Trans Circuit Syst Video Technol 19(6):911–916CrossRefGoogle Scholar
  35. Cucchiara R, Grana C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans Pattern Anal Mach Intel 25(10):1337–1342CrossRefGoogle Scholar
  36. Cui L, Li K, Chen J, Li Z (2011) Abnormal event detection in traffic video surveillance based on local features. In: 4th international congress on image and signal processing (CISP), 2011, IEEE, vol 1, pp 362–366Google Scholar
  37. Datta A, Shah M, Lobo NDV (2002) Person-on-person violence detection in video data. In: Proceedings of the 16th international conference on pattern recognition, 2002, IEEE, vol 1, pp 433–438Google Scholar
  38. Dick AR, Brooks MJ (2003) Issues in automated visual surveillance. In: International conference on digital image computing: techniques and applicationsGoogle Scholar
  39. Dimitropoulos K, Barmpoutis P, Grammalidis N (2015) Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans Circuit Syst Video Technol 25(2):339–351CrossRefGoogle Scholar
  40. Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. In: Computer Vision, ECCV 2000. Springer, Berlin, pp 751–767Google Scholar
  41. Elhamod M, Levine MD (2013) Automated real-time detection of potentially suspicious behavior in public transport areas. IEEE Trans Intel Transp Syst 14(2):688–699CrossRefGoogle Scholar
  42. Ellingsen K (2008) Salient event-detection in video surveillance scenarios. In: Proceedings of the 1st ACM workshop on analysis and retrieval of events/actions and workflows in video streams. ACM, pp 57–64Google Scholar
  43. Evangelio RH, Sikora T (2010) Static object detection based on a dual background model and a finite-state machine. EURASIP J Image Video Process 2011(1):858,502Google Scholar
  44. Fan Q, Pankanti S (2012) Robust foreground and abandonment analysis for large-scale abandoned object detection in complex surveillance videos. In: IEEE ninth international conference on advanced video and signal- based surveillance (AVSS), 2012, IEEE, pp 58–63Google Scholar
  45. Fan Q, Gabbur P, Pankanti S (2013) Relative attributes for large-scale abandoned object detection. In: IEEE international conference on computer vision (ICCV), 2013, IEEE, pp 2736–2743Google Scholar
  46. Femi PS, Thaiyalnayaki K (2013) Detection of abandoned and stolen objects in videos using mixture of gaussians. Int J Comput Appl 70(10):18–21Google Scholar
  47. Fern’andez-Caballero A, Castillo JC, Rodr’ıguez-S’anchez JM (2012) Human activity monitoring by local and global finite state machines. Expert Syst Appl 39(8):6982–6993CrossRefGoogle Scholar
  48. Ferryman J, Hogg D, Sochman J, Behera A, Rodriguez-Serrano JA, Worgan S, Li L, Leung V, Evans M, Cornic P et al (2013) Robust abandoned object detection integrating wide area visual surveillance and social context. Pattern Recogn Lett 34(7):789–798CrossRefGoogle Scholar
  49. Firesense project protection of cultural heritage (2009). http://www.firesense.eu/
  50. Foggia P, Saggese A, Vento M(2015) Real-time fire detection for video surveillance applications using a combination of experts based on color, shape and motion. IEEE Trans Circuit Syst Video Technol 25(9):1545–1556Google Scholar
  51. Foo SY (1996) A rule-based machine vision system for fire detection in aircraft dry bays and engine compartments. Knowl Based Syst 9(8):531–540CrossRefGoogle Scholar
  52. Foresti GL, Marcenaro L, Regazzoni CS (2002) Automatic detection and indexing of videoevent shots for surveillance applications. IEEE Trans Multimed 4(4):459–471CrossRefGoogle Scholar
  53. Foroughi H, Aski BS, Pourreza H (2008a) Intelligent video surveillance for monitoring fall detection of elderly in home environments. In: 11th international conference on computer and information technology. ICCIT 2008, IEEE, pp 219–224Google Scholar
  54. Foroughi H, Rezvanian A, Paziraee A (2008b) Robust fall detection using human shape and multi-class support vector machine. In: Sixth Indian conference on computer vision, graphics and image processing, 2008. ICVGIP’08, IEEE, pp 413–420Google Scholar
  55. Foucher S, Lalonde M, Gagnon L (2011) A system for airport surveillance: detection of people running, abandoned objects, and pointing gestures. In: International society for optics and photonics SPIE defense, security, and sensing, p 805610Google Scholar
  56. Ghazal M, Vázquez C, Amer A (2007) Real-time automatic detection of vandalism behavior in video sequences. In: IEEE international conference on systems, man and cybernetics, 2007. ISIC, IEEE, pp 1056–1060Google Scholar
  57. Ghazal M, VáZquez C, Amer A (2012) Real-time vandalism detection by monitoring object activities. Multimed Tools Appl 58(3):585–611CrossRefGoogle Scholar
  58. Gouaillier V, Fleurant A (2009) Intelligent video surveillance: promises and challenges. Technological and commercial intelligence report. CRIM Technôpole Def Secur 456:468–561Google Scholar
  59. Gowsikhaa D, Manjunath AS, Abirami S (2012) Suspicious human activity detection from surveillance videos. Int J Internet Distrib Comput Syst 2(2):141–149Google Scholar
  60. Gracia IS, Suarez OD, Garcia GB, Kim TK (2015) Fast fight detection. PLoS ONE 10(4):1–19Google Scholar
  61. Gubbi J, Marusic S, Palaniswami M (2009) Smoke detection in video using wavelets and support vector machines. Fire Saf J 44(8):1110–1115CrossRefGoogle Scholar
  62. Guler S, Silverstein J, Pushee IH, et al (2007) Stationary objects in multiple object tracking. In: IEEE conference on advanced video and signal based surveillance. AVSS 2007, IEEE, pp 248–253Google Scholar
  63. Habiboǧlu YH, Günay O, Çetin AE (2012) Covariance matrix-based fire and flame detection method in video. Mach Vis Appl 23(6):1103–1113CrossRefGoogle Scholar
  64. Han J, Ma KK (2002) Fuzzy color histogram and its use in color image retrieval. IEEE Trans Image Process 11(8):944–952CrossRefGoogle Scholar
  65. Höferlin M, Höferlin B, Weiskopf D, Heidemann G (2015) Uncertainty-aware video visual analytics of tracked moving objects. J Spatial Inf Sci 2:87–117Google Scholar
  66. Hsieh CT, Hsu SB, Han CC, Fan KC (2011) Abnormal event detection using trajectory features. J Inf Technol Appl 5(1):22–27Google Scholar
  67. Human fall detection dataset (2014). http://foe.mmu.edu.my/digitalhome/fallvideo.zip
  68. Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern Part C Appl Rev 34(3):334–352CrossRefGoogle Scholar
  69. Ibrahim N, Mokri SS, Siong LY, Mustafa MM, Hussain A (2010) Snatch theft detection using low level. In: Proceedings of the world congress on engineering, vol 2Google Scholar
  70. Ibrahim N, Mustafa MM, Mokri SS, Siong LY, Hussain A (2012) Detection of snatch theft based on temporal differences in motion flow field orientation histograms. Int J Adv Comput Technol 4(12):308–317Google Scholar
  71. i-lids dataset for advanced video and signal based (2007) surveillance, AVSS 2007. http://www.eecs.qmul.ac.uk/andrea/avss2007v.html
  72. Jalal AS, Singh V (2012) The state-of-the-art in visual object tracking. Informatica 36(3):227–248Google Scholar
  73. Jiang F, Yuan J, Tsaftaris SA, Katsaggelos AK (2011) Anomalous video event detection using spatiotemporal context. Comput Vis Image Underst 115(3):323–333CrossRefGoogle Scholar
  74. Juang CF, Chang CM (2007) Human body posture classification by a neural fuzzy network and home care system application. IEEE Trans Syst ManCybern Part A Syst Humans 37(6):984–994CrossRefGoogle Scholar
  75. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Fluids Eng 82(1):35–45Google Scholar
  76. Kamijo S, Matsushita Y, Ikeuchi K, Sakauchi M (2000) Traffic monitoring and accident detection at intersections. IEEE Trans Intell Transp Syst 1(2):108–118CrossRefGoogle Scholar
  77. Kausalya K, Chitrakala S (2012) Idle object detection in video for banking ATM applications. Res J Appl Sci Eng Technol 4(4):5350–5356Google Scholar
  78. Ke SR, Thuc HLU, Lee YJ, Hwang JN, Yoo JH, Choi KH (2013) A review on video-based human activity recognition. Computers 2(2):88–131CrossRefGoogle Scholar
  79. Khan Z, Sohn W et al (2011) Abnormal human activity recognition system based on r-transform and kernel discriminant technique for elderly home care. IEEE Trans Consumer Electron 57(4):1843–1850CrossRefGoogle Scholar
  80. Kim K, Chalidabhongse TH, Harwood D, Davis L (2005) Real-time foreground–background segmentation using codebook model. Real-time Imaging 11(3):172–185CrossRefGoogle Scholar
  81. Kitagawa G (1987) Non-gaussian state space modeling of nonstationary time series. J Am Stat Assoc 82(400):1032–1041MathSciNetzbMATHGoogle Scholar
  82. Kong H, Audibert JY, Ponce J (2010) Detecting abandoned objects with a moving camera. IEEE Trans Image Process 19(8):2201–2210MathSciNetCrossRefzbMATHGoogle Scholar
  83. Lai TY, Kuo JY, Fanjiang YY, Ma SP, Liao YH (2012) Robust little flame detection on real-time video surveillance system. In: Third international conference on innovations in bio-inspired computing and applications (IBICA), 2012, IEEE, pp 139–143Google Scholar
  84. Lavee G, Khan L, Thuraisingham B (2005) A framework for a video analysis tool for suspicious event detection, pp 79–84Google Scholar
  85. Lavee G, Khan L, Thuraisingham B (2007) A framework for a video analysis tool for suspicious event detection. Multimed Tools Appl 35(1):109–123CrossRefGoogle Scholar
  86. Lee JT, Ryoo MS, Riley M, Aggarwal J (2009) Real-time illegal parking detection in outdoor environments using 1-d transformation. IEEE Trans Circuit Syst Video Technol 19(7):1014–1024CrossRefGoogle Scholar
  87. Lei W, Liu J (2013) Early fire detection in coalmine based on video processing. Proceedings of the 2012 international conference on communication, electronics and automation engineering. Springer, Berlin, pp 239–245CrossRefGoogle Scholar
  88. Li Q, Mao Y, Wang Z, Xiang W (2009) Robust real-time detection of abandoned and removed objects. In: Fifth international conference on image and graphics, 2009. ICIG’09, IEEE, pp 156–161Google Scholar
  89. Li X, Zhang C, Zhang D (2010) Abandoned objects detection using double illumination invariant foreground masks. In: 20th international conference on pattern recognition (ICPR), 2010, IEEE, pp 436–439Google Scholar
  90. Liao HH, Chang JY, Chen LG (2008) A localized approach to abandoned luggage detection with foreground-mask sampling. In: IEEE Fifth international conference on advanced video and signal based surveillance, 2008. AVSS’08., IEEE, pp 132–139Google Scholar
  91. Lin CW, Ling ZH, Chang YC, Kuo CJ, (2005) Compressed-domain fall incident detection for intelligent home surveillance. In: IEEE international symposium on circuits and systems, (2005) ISCAS 2005, IEEE, pp 3781–3784Google Scholar
  92. Liu CL, Lee CH, Lin PM (2010) A fall detection system using k-nearest neighbor classifier. Expert Syst Appl 37(10):7174–7181CrossRefGoogle Scholar
  93. Liu H, Zuo C (2012) An improved algorithm of automatic fall detection. AASRI Procedia 1:353–358CrossRefGoogle Scholar
  94. Lo B, Velastin S (2001) Automatic congestion detection system for underground platforms. In: Proceedings of 2001 international symposium on intelligent multimedia, video and speech processing, 2001, IEEE, pp 158–161Google Scholar
  95. M E (2011) Caviar dataset 2011: Fight and one man down demo. http://www.cim.mcgill.ca/mndhamod/thesisvideos/caviarfightonemandown.avi
  96. Maddalena L, Petrosino A (2013) Stopped object detection by learning foreground model in videos. IEEE Trans Neural Netw Learn Syst 24(5):723–735CrossRefGoogle Scholar
  97. Magno M, Tombari F, Brunelli D, Di Stefano L, Benini L (2009) Multimodal abandoned/ removed object detection for low power video surveillance systems. In: Sixth IEEE international conference on advanced video and signal based surveillance, 2009. AVSS’09, IEEE, pp 188–193Google Scholar
  98. Manjunatha KC, Mohana HS, Vijaya PA (2015) Implementation of computer vision based industrial fire safety automation by using neuro-fuzzy algorithms. Int J Inf Technol Comput Sci 7(4):14–27Google Scholar
  99. McHugh JM, Konrad J, Saligrama V, Jodoin PM (2009) Foreground-adaptive background subtraction. Signal Process Lett IEEE 16(5):390–393CrossRefGoogle Scholar
  100. Mesh (2007), multimedia semantic syndication for enhanced news service. In: IST 6th framework programme European Commission Project. http://www.mesh-ip.eu/
  101. Miguel JCS, Mart’ınez JM (2008) Robust unattended and stolen object detection by fusing simple algorithms. In: IEEE fifth international conference on advanced video and signal based surveillance, 2008. AVSS’08, IEEE, pp 18–25Google Scholar
  102. Mukherjee D, Wu Q, Nguyen TM (2014) Gaussian mixture model with advanced distance measure based on support weights and histogram of gradients for background suppression. IEEE Trans Ind Inf 10(2):1086–1096CrossRefGoogle Scholar
  103. Nam Y (2016) Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes. Multimed Tools Appl 75(12):7003–7028Google Scholar
  104. Nasution AH, Emmanuel S (2007) Intelligent video surveillance for monitoring elderly in home environments. In: IEEE 9th workshop on multimedia signal processing, 2007. MMSP 2007, IEEE, pp 203–206Google Scholar
  105. Nguyen TT, Cho MC, Lee TS (2009) Automatic fall detection using wearable biomedical signal measurement terminal. In: Annual international conference of the IEEE engineering in medicine and biology society, 2009. EMBC 2009, IEEE, pp 5203–5206Google Scholar
  106. Pavithradevi MK, Aruljothi S (2014) Detection of suspicious activities in public areas using staged matching technique. IJAICT 1(1):140–144Google Scholar
  107. Penmetsa S, Minhuj F, Singh A, Omkar SN (2014) Autonomous UAV for suspicious action detection using pictorial human pose estimation and classification ELCVIA Electron Lett Comput Vis Image Anal 13(1):18–32Google Scholar
  108. Pets 2001 benchmark data (2001). http://www.cvg.rdg.ac.uk/pets2001/
  109. Pets 2006 benchmark data (2006). http://www.cvg.rdg.ac.uk/PETS2006/data.html
  110. Pets 2007 benchmark data (2007). http://www.cvg.rdg.ac.uk/pets2006/data.html
  111. Piccardi M (2004) Background subtraction techniques: a review. In: IEEE international conference on systems, man and cybernetics, 2004, IEEE, vol 4, pp 3099–3104Google Scholar
  112. Popoola OP, Wang K (2012) Video-based abnormal human behavior recognitiona review. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):865–878CrossRefGoogle Scholar
  113. Poppe R (2010) A survey on vision-based human action recognition. Image Vision Comput 28(6):976–990CrossRefGoogle Scholar
  114. Porikli F (2007) Detection of temporarily static regions by processing video at different frame rates. In: IEEE conference on advanced video and signal based surveillance, 2007. AVSS 2007, IEEE, pp 236–241Google Scholar
  115. Porikli F, Ivanov Y, Haga T (2008) Robust abandoned object detection using dual foregrounds. EURASIP J Adv Signal Process 2008:30zbMATHGoogle Scholar
  116. Prabhakar G, Ramasubramanian B (2012) An efficient approach for real time tracking of intruder and abandoned object in video surveillance system. Int J Comput Appl 54(17):22–27Google Scholar
  117. Pteri FSHM R (2012) Dyntex: a comprehensive database of dynamic textures 2012. Pattern Recogn Lett. http://projects.cwi.nl/dyntex/database.html
  118. Rabiner LR (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRefGoogle Scholar
  119. Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuit Syst Video Technol 21(5):611–622CrossRefGoogle Scholar
  120. Ryoo M, Aggarwal J (2011) Stochastic representation and recognition of high-level group activities. Int J Comput Vision 93(2):183–200MathSciNetCrossRefzbMATHGoogle Scholar
  121. Sacchi C, Regazzoni CS (2000) A distributed surveillance system for detection of abandoned objects in unmanned railway environments. IEEE Trans Veh Technol 49(5):2013–2026CrossRefGoogle Scholar
  122. Sadek S, Al-Hamadi A, Michaelis B, Sayed U (2010) A statistical framework for real-time traffic accident recognition. J Signal Inf Process 1(01):77Google Scholar
  123. Sadeky S, Al-Hamadiy A, Michaelisy B, Sayed U (2010) Real-time automatic traffic accident recognition using HFG. In: 20th International conference on pattern recognition (ICPR), 2010, IEEE, pp 3348–3351Google Scholar
  124. Sajith K, Nair KR (2013) Abandoned or removed objects detection from surveillance video using codebook. Int J Eng Res Technol 2:401–406Google Scholar
  125. Sample fire and smoke video clips (2009). http://signal.ee.bilkent.edu.tr/visifire/demo/sampleclips.html
  126. SanMiguel J, Caro L, Martinez J (2012) Pixel-based colour contrast for abandoned and stolen object discrimination in video surveillance. Electron Lett 48(2):86–87CrossRefGoogle Scholar
  127. Seebamrungsat J, Praising S, Riyamongkol P (2014) Fire detection in the buildings using image processing. In: Third ICT international student project conference (ICT-ISPC), 2014, IEEE, pp 95–98Google Scholar
  128. Singh R, Vishwakarma S, Agrawal A, Tiwari M (2010) Unusual activity detection for videosurveillance. In: Proceedings of the first international conference on intelligent interactive technologies and multimedia. ACM, pp 297–305Google Scholar
  129. Snoek J, Hoey J, Stewart L, Zemel RS, Mihailidis A (2009) Automated detection of unusual events on stairs. Image Vis Comput 27(1):153–166CrossRefGoogle Scholar
  130. Soomro K, Zamir AR, Shah M (2012) Ucf101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402
  131. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE computer society conference on computer vision and pattern recognition, 1999, IEEE, vol 2Google Scholar
  132. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intel 22(8):747–757CrossRefGoogle Scholar
  133. Sternig S, Roth PM, Grabner H, Bischof H (2009) Robust adaptive classifier grids for object detection from static cameras. In: Proceedings computer vision winter workshopGoogle Scholar
  134. Sujith B (2014) Crime detection and avoidance in ATM: a new framework. Int J Comput Sci Inf Technol 5(5):6068–6071Google Scholar
  135. Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Analysis and modeling of faces and gestures. Springer, Berlin, pp 168–182Google Scholar
  136. Tejas Naren TN VKSSLSC Shankar SiddharthKA (2014) Abandoned object detection for automated video surveillance using hadoop. Int J Adv Res Electr Electr Instrum Eng 3:101–107Google Scholar
  137. Thome N, Miguet S (2006) A hhmm-based approach for robust fall detection. In: 9th International conference on control, automation, robotics and vision, 2006. ICARCV’06, IEEE, pp 1–8Google Scholar
  138. Thome N, Miguet S, Ambellouis S (2008) A real-time, multiview fall detection system: alhmm-based approach. IEEE Trans Circuit Syst Video Technol 18(11):1522–1532CrossRefGoogle Scholar
  139. Tian Y, Feris RS, Liu H, Hampapur A, Sun MT (2011) Robust detection of abandoned and removed objects in complex surveillance videos. IEEE Trans Syst Man Cybern Part C Appl Rev 41(5):565–576CrossRefGoogle Scholar
  140. Tian Y, Senior A, Lu M (2012) Robust and efficient foreground analysis in complex surveillance videos. Mach Vis Appl 23(5):967–983CrossRefGoogle Scholar
  141. Tomasi C, Kanade T (1991) Detection and tracking of point features. School of Computer Science, Carnegie Mellon Univ, PittsburghGoogle Scholar
  142. Töreyin BU, Dedeoglu Y et al (2005) Flame detection in video using hidden markov models. In: IEEE international conference on image processing, 2005. ICIP 2005, IEEE, vol 2, pp II–1230Google Scholar
  143. Töreyin BU, Dedeoglu Y, Güdükbay U, Cetin AE (2006) Computer vision based method for real-time fire and flame detection. Pattern Recogn Lett 27(1):49–58CrossRefGoogle Scholar
  144. Töreyin BU, et al (2007) Online detection of fire in video. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07, IEEE, pp 1–5Google Scholar
  145. Traffic videos from the next generation simulation (2007). http://ngsim.camsys.com/
  146. Tripathi RK, Jalal AS (2014) A framework for suspicious object detection from surveillance video. Int J Mach Intel Sensory Signal Process 1(3):251–266Google Scholar
  147. Tripathi RK, Jalal AS, Bhatnagar C (2013) A framework for abandoned object detection from video surveillance. In: Fourth national conference on computer vision, pattern recognition, image processing and graphics (NCVPRIPG), 2013, IEEE, pp 1–4Google Scholar
  148. Tripathi V, Gangodkar D, Latta V, Mittal A (2015) Robust abnormal event recognition via motion and shape analysis at ATM installations. J Electr Comput Eng 2015. doi: 10.1155/2015/502737
  149. Trecvid 2010 evaluation for surveillance detection (2010). http://www.itl.nist.gov/iad/mig/tests/trecvid/2010/
  150. Vezzani R, Cucchiara R (2010) Video surveillance online repository (visor): an integrated framework. Multimed Tools Appl 50(2):359–380CrossRefGoogle Scholar
  151. Vicente J, Guillemant P (2002) An image processing technique for automatically detecting forest fire. Int J Therm Sci 41(12):1113–1120CrossRefGoogle Scholar
  152. Vu VT, Brémond F, Thonnat M (2002) Temporal constraints for video interpretation. In 15th European conference on artificial intelligenceGoogle Scholar
  153. Wang X, Ma X, Grimson E (2009) Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Trans Pattern Anal Mach Intel 31(2):539–555CrossRefGoogle Scholar
  154. Wang S, Chen L, Zhou Z, Sun X, Dong J (2016) Human fall detection in surveillance video based on PCANet. Multimed Tool Appl 75(19):11603–11613Google Scholar
  155. Wieser D, Brupbacher T (2001) Smoke detection in tunnels using video images. NIST SPECIAL PUBLICATION SP, pp 79–90Google Scholar
  156. Wiliem A, Madasu V, Boles W, Yarlagadda P (2012) A suspicious behaviour detection using a context space model for smart surveillance systems. Comput Vis Image Underst 116(2):194–209CrossRefGoogle Scholar
  157. Willems J, Debard G, Bonroy B, Vanrumste B, Goedemé T (2009) How to detect human fall in video? In: An overview, positioning and context awareness international conference, POCAGoogle Scholar
  158. Wren CR, Azarbayejani A, Darrell T, Pentland AP (1997) Pfinder: real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785CrossRefGoogle Scholar
  159. Yang Z, Rothkrantz L (2011) Surveillance system using abandoned object detection. In: Proceedings of the 12th international conference on computer systems and technologies. ACM, pp 380–386Google Scholar
  160. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13CrossRefGoogle Scholar
  161. Yogameena B, Deepila G, Mehjabeen J (2012) RVM based human fall analysis for video surveillance applications? Res J Appl Sci Eng Technol 4(24):5361–5366Google Scholar
  162. Yu M, Rhuma A, Naqvi SM, Wang L, Chambers J (2012) A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. IEEE Trans Inf Technol Biomed 16(6):1274–1286CrossRefGoogle Scholar
  163. Yuan F (2008) A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recogn Lett 29(7):925–932CrossRefGoogle Scholar
  164. Yuan F (2010) An integrated fire detection and suppression system based on widely available video surveillance. Mach Vis Appl 21(6):941–948CrossRefGoogle Scholar
  165. Zhou Y, Benois-Pineau J, Nicolas H (2010) Multi-object particle filter tracking with automatic event analysis. In: Proceedings of the first ACM international workshop on analysis and retrieval of tracked events and motion in imagery streams. ACM, pp 21–26Google Scholar
  166. Zhou Z, Chen X, Chung YC, He Z, Han TX, Keller JM (2008) Activity analysis, summarization, and visualization for indoor human activity monitoring. IEEE Trans Circuit Syst Video Technol 18(11):1489–1498CrossRefGoogle Scholar
  167. Ziaeefard M, Bergevin R (2015) Semantic human activity recognition: a literature review. Pattern Recogn 8(48):2329–2345CrossRefGoogle Scholar
  168. Zin TT, Tin P, Toriu T, Hama H (2012a) A novel probabilistic video analysis for stationary object detection in video surveillance systems. IAENG Int J Comput Sci 39(3):295–306Google Scholar
  169. Zin TT, Tin P, Toriu T, Hama H (2012b) A probability-based model for detecting abandoned objects in video surveillance systems. In: Proceedings of the world congress on engineering, vol 2Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Rajesh Kumar Tripathi
    • 1
  • Anand Singh Jalal
    • 1
  • Subhash Chand Agrawal
    • 1
  1. 1.Department of CEA, IETGLA UniversityMathuraIndia

Personalised recommendations