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Suspicious human activity recognition: a review

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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.

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References

  • Achkar F, Amer A (2007) Hysteresis-based selective gaussian mixture models for real- timebackground maintenance. SPIE Vis Commun Image Process 6508:J1–J11

    Google Scholar 

  • 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):555–560

    Article  Google Scholar 

  • Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv (CSUR) 43(3):16

    Article  Google Scholar 

  • 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 26

  • 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 709–712

  • 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 474–479

  • Allgovision (2015) Advanced video analytics for traffic/parking management. http://www.allgovision.com/traffic-praking.php

  • Amer A (2005) Voting-based simultaneous tracking of multiple video objects. IEEE Trans Circuit Syst Video Technol 15(11):1448–1462

    Article  Google Scholar 

  • 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 6388–6391

  • Asodds (2011) An abandoned and stolen object discrimination dataset. http://wwwvpu.eps.uam.es/asodds

  • 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):290–300

    Article  Google Scholar 

  • Auvinet E, Rougier C, Meunier J, St-Anaud A, Rousseau J (2010) Multiple Cameras Fall Data Set. DIRO-Universite de Montrial, Technical Report 1350

  • AVSS (2007) http://www.eecs.qmul.ac.uk/andrea/avss2007d.html

  • Bangare PS, Uke NJ, Bangare SL (2012) Implementation of abandoned object detection in real time environment. Int J Comput Appl 57(12):13–16

  • 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 59–66

  • Benezeth Y, Jodoin PM, Saligrama V (2011) Abnormality detection using low-level co-occurring events. Pattern Recogn Lett 32(3):423–431

    Article  Google Scholar 

  • Bevilacqua A, Bevilacqua R (2002) Effective object segmentation in a traffic monitoring application. In: ICVGIP 2002 conference proceedings, Ahmedabad, India, Citeseer

  • Bhargava M, Chen CC, Ryoo MS, Aggarwal JK (2009) Detection of object abandonment using temporal logic. Mach Vis Appl 20(5):271–281

    Article  Google Scholar 

  • 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 3775–3780

  • Borges PVK, Izquierdo E (2010) A probabilistic approach for vision-based fire detection in videos. IEEE Trans Circuit Syst Video Technol 20(5):721–731

    Article  Google Scholar 

  • Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev 11:31–66

    Article  MATH  Google Scholar 

  • 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):974–982

    Article  Google Scholar 

  • 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):206–224

    Article  Google Scholar 

  • 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 563–570

  • Caviar fall on floor dataset (2004). http://homepages.inf.ed.ac.uk/rbf/caviardata1/

  • Caviar Left Bag (2004) http://www.multitel.be/va/cantata/leftobject/

  • 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 147–158

  • Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  • 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 2664–2667

  • 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 1707–1710

  • 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 3485–3488

  • Chien SY, Ma SY, Chen LG (2002) Efficient moving object segmentation algorithm using background registration technique. IEEE Trans Circuit Syst Video Technol 12(7):577–586

    Article  Google Scholar 

  • 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–meta

  • Chua JL, Chang YC, Lim WK (2013) A simple vision-based fall detection technique for indoor video surveillance. SIViP 9(3):623–633

    Article  Google Scholar 

  • 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 3546–3549

  • 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):911–916

    Article  Google Scholar 

  • 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):1337–1342

    Article  Google Scholar 

  • 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 362–366

  • Cvsg (2008) http://www.vpu.eps.uam.es/cvsg/

  • 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 433–438

  • Dick AR, Brooks MJ (2003) Issues in automated visual surveillance. In: International conference on digital image computing: techniques and applications

  • 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):339–351

    Article  Google Scholar 

  • Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. In: Computer Vision, ECCV 2000. Springer, Berlin, pp 751–767

  • Elhamod M, Levine MD (2013) Automated real-time detection of potentially suspicious behavior in public transport areas. IEEE Trans Intel Transp Syst 14(2):688–699

    Article  Google Scholar 

  • 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 57–64

  • 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):858,502

    Google Scholar 

  • 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 58–63

  • 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 2736–2743

  • Fastdata (2014) http://fire.nist.gov/fastdata

  • Femi PS, Thaiyalnayaki K (2013) Detection of abandoned and stolen objects in videos using mixture of gaussians. Int J Comput Appl 70(10):18–21

  • 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):6982–6993

    Article  Google Scholar 

  • 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):789–798

    Article  Google Scholar 

  • Firesense project protection of cultural heritage (2009). http://www.firesense.eu/

  • 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):1545–1556

  • Foo SY (1996) A rule-based machine vision system for fire detection in aircraft dry bays and engine compartments. Knowl Based Syst 9(8):531–540

    Article  Google Scholar 

  • Foresti GL, Marcenaro L, Regazzoni CS (2002) Automatic detection and indexing of videoevent shots for surveillance applications. IEEE Trans Multimed 4(4):459–471

    Article  Google Scholar 

  • 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 219–224

  • 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 413–420

  • 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 805610

  • 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 1056–1060

  • Ghazal M, VáZquez C, Amer A (2012) Real-time vandalism detection by monitoring object activities. Multimed Tools Appl 58(3):585–611

    Article  Google Scholar 

  • Gouaillier V, Fleurant A (2009) Intelligent video surveillance: promises and challenges. Technological and commercial intelligence report. CRIM Technôpole Def Secur 456:468–561

    Google Scholar 

  • Gowsikhaa D, Manjunath AS, Abirami S (2012) Suspicious human activity detection from surveillance videos. Int J Internet Distrib Comput Syst 2(2):141–149

    Google Scholar 

  • Gracia IS, Suarez OD, Garcia GB, Kim TK (2015) Fast fight detection. PLoS ONE 10(4):1–19

  • Gubbi J, Marusic S, Palaniswami M (2009) Smoke detection in video using wavelets and support vector machines. Fire Saf J 44(8):1110–1115

    Article  Google Scholar 

  • 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 248–253

  • Habiboǧlu YH, Günay O, Çetin AE (2012) Covariance matrix-based fire and flame detection method in video. Mach Vis Appl 23(6):1103–1113

    Article  Google Scholar 

  • Han J, Ma KK (2002) Fuzzy color histogram and its use in color image retrieval. IEEE Trans Image Process 11(8):944–952

    Article  Google Scholar 

  • Höferlin M, Höferlin B, Weiskopf D, Heidemann G (2015) Uncertainty-aware video visual analytics of tracked moving objects. J Spatial Inf Sci 2:87–117

    Google Scholar 

  • Hsieh CT, Hsu SB, Han CC, Fan KC (2011) Abnormal event detection using trajectory features. J Inf Technol Appl 5(1):22–27

    Google Scholar 

  • Human fall detection dataset (2014). http://foe.mmu.edu.my/digitalhome/fallvideo.zip

  • 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):334–352

    Article  Google Scholar 

  • 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 2

  • 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):308–317

  • i-lids dataset for advanced video and signal based (2007) surveillance, AVSS 2007. http://www.eecs.qmul.ac.uk/andrea/avss2007v.html

  • Jalal AS, Singh V (2012) The state-of-the-art in visual object tracking. Informatica 36(3):227–248

  • Jiang F, Yuan J, Tsaftaris SA, Katsaggelos AK (2011) Anomalous video event detection using spatiotemporal context. Comput Vis Image Underst 115(3):323–333

    Article  Google Scholar 

  • 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):984–994

    Article  Google Scholar 

  • Kalman RE (1960) A new approach to linear filtering and prediction problems. J Fluids Eng 82(1):35–45

    Google Scholar 

  • Kamijo S, Matsushita Y, Ikeuchi K, Sakauchi M (2000) Traffic monitoring and accident detection at intersections. IEEE Trans Intell Transp Syst 1(2):108–118

    Article  Google Scholar 

  • Kausalya K, Chitrakala S (2012) Idle object detection in video for banking ATM applications. Res J Appl Sci Eng Technol 4(4):5350–5356

  • Ke SR, Thuc HLU, Lee YJ, Hwang JN, Yoo JH, Choi KH (2013) A review on video-based human activity recognition. Computers 2(2):88–131

    Article  Google Scholar 

  • 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):1843–1850

    Article  Google Scholar 

  • Kim K, Chalidabhongse TH, Harwood D, Davis L (2005) Real-time foreground–background segmentation using codebook model. Real-time Imaging 11(3):172–185

    Article  Google Scholar 

  • Kitagawa G (1987) Non-gaussian state space modeling of nonstationary time series. J Am Stat Assoc 82(400):1032–1041

    MathSciNet  MATH  Google Scholar 

  • Kong H, Audibert JY, Ponce J (2010) Detecting abandoned objects with a moving camera. IEEE Trans Image Process 19(8):2201–2210

    Article  MathSciNet  MATH  Google Scholar 

  • 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 139–143

  • Lavee G, Khan L, Thuraisingham B (2005) A framework for a video analysis tool for suspicious event detection, pp 79–84

  • Lavee G, Khan L, Thuraisingham B (2007) A framework for a video analysis tool for suspicious event detection. Multimed Tools Appl 35(1):109–123

    Article  Google Scholar 

  • 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):1014–1024

    Article  Google Scholar 

  • 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 239–245

    Chapter  Google Scholar 

  • 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 156–161

  • 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 436–439

  • 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 132–139

  • 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 3781–3784

  • Liu CL, Lee CH, Lin PM (2010) A fall detection system using k-nearest neighbor classifier. Expert Syst Appl 37(10):7174–7181

    Article  Google Scholar 

  • Liu H, Zuo C (2012) An improved algorithm of automatic fall detection. AASRI Procedia 1:353–358

    Article  Google Scholar 

  • 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 158–161

  • M E (2011) Caviar dataset 2011: Fight and one man down demo. http://www.cim.mcgill.ca/mndhamod/thesisvideos/caviarfightonemandown.avi

  • Maddalena L, Petrosino A (2013) Stopped object detection by learning foreground model in videos. IEEE Trans Neural Netw Learn Syst 24(5):723–735

    Article  Google Scholar 

  • 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 188–193

  • 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):14–27

  • McHugh JM, Konrad J, Saligrama V, Jodoin PM (2009) Foreground-adaptive background subtraction. Signal Process Lett IEEE 16(5):390–393

    Article  Google Scholar 

  • Mesh (2007), multimedia semantic syndication for enhanced news service. In: IST 6th framework programme European Commission Project. http://www.mesh-ip.eu/

  • 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 18–25

  • 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):1086–1096

    Article  Google Scholar 

  • Nam Y (2016) Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes. Multimed Tools Appl 75(12):7003–7028

  • 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 203–206

  • 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 5203–5206

  • Pavithradevi MK, Aruljothi S (2014) Detection of suspicious activities in public areas using staged matching technique. IJAICT 1(1):140–144

  • 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):18–32

  • Pets 2001 benchmark data (2001). http://www.cvg.rdg.ac.uk/pets2001/

  • Pets 2006 benchmark data (2006). http://www.cvg.rdg.ac.uk/PETS2006/data.html

  • Pets 2007 benchmark data (2007). http://www.cvg.rdg.ac.uk/pets2006/data.html

  • Piccardi M (2004) Background subtraction techniques: a review. In: IEEE international conference on systems, man and cybernetics, 2004, IEEE, vol 4, pp 3099–3104

  • Popoola OP, Wang K (2012) Video-based abnormal human behavior recognitiona review. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):865–878

    Article  Google Scholar 

  • Poppe R (2010) A survey on vision-based human action recognition. Image Vision Comput 28(6):976–990

    Article  Google Scholar 

  • 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 236–241

  • Porikli F, Ivanov Y, Haga T (2008) Robust abandoned object detection using dual foregrounds. EURASIP J Adv Signal Process 2008:30

    MATH  Google Scholar 

  • 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):22–27

    Google Scholar 

  • Pteri FSHM R (2012) Dyntex: a comprehensive database of dynamic textures 2012. Pattern Recogn Lett. http://projects.cwi.nl/dyntex/database.html

  • Qmul junction dataset (2010). http://www.eecs.qmul.ac.uk/ccloy/downloadsqmuljunction.html

  • Rabiner LR (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  • 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):611–622

    Article  Google Scholar 

  • Ryoo M, Aggarwal J (2011) Stochastic representation and recognition of high-level group activities. Int J Comput Vision 93(2):183–200

    Article  MathSciNet  MATH  Google Scholar 

  • Sacchi C, Regazzoni CS (2000) A distributed surveillance system for detection of abandoned objects in unmanned railway environments. IEEE Trans Veh Technol 49(5):2013–2026

    Article  Google Scholar 

  • 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):77

    Google Scholar 

  • 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 3348–3351

  • Sajith K, Nair KR (2013) Abandoned or removed objects detection from surveillance video using codebook. Int J Eng Res Technol 2:401–406

  • Sample fire and smoke video clips (2009). http://signal.ee.bilkent.edu.tr/visifire/demo/sampleclips.html

  • SanMiguel J, Caro L, Martinez J (2012) Pixel-based colour contrast for abandoned and stolen object discrimination in video surveillance. Electron Lett 48(2):86–87

    Article  Google Scholar 

  • 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 95–98

  • 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 297–305

  • Snoek J, Hoey J, Stewart L, Zemel RS, Mihailidis A (2009) Automated detection of unusual events on stairs. Image Vis Comput 27(1):153–166

    Article  Google Scholar 

  • 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

  • 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 2

  • Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intel 22(8):747–757

    Article  Google Scholar 

  • Sternig S, Roth PM, Grabner H, Bischof H (2009) Robust adaptive classifier grids for object detection from static cameras. In: Proceedings computer vision winter workshop

  • Sujith B (2014) Crime detection and avoidance in ATM: a new framework. Int J Comput Sci Inf Technol 5(5):6068–6071

    Google Scholar 

  • 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 168–182

  • Tejas Naren TN VKSSLSC Shankar SiddharthKA (2014) Abandoned object detection for automated video surveillance using hadoop. Int J Adv Res Electr Electr Instrum Eng 3:101–107

  • 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 1–8

  • 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):1522–1532

    Article  Google Scholar 

  • 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):565–576

    Article  Google Scholar 

  • Tian Y, Senior A, Lu M (2012) Robust and efficient foreground analysis in complex surveillance videos. Mach Vis Appl 23(5):967–983

    Article  Google Scholar 

  • Tomasi C, Kanade T (1991) Detection and tracking of point features. School of Computer Science, Carnegie Mellon Univ, Pittsburgh

    Google Scholar 

  • 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–1230

  • 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):49–58

    Article  Google Scholar 

  • 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 1–5

  • Traffic videos from the next generation simulation (2007). http://ngsim.camsys.com/

  • Tripathi RK, Jalal AS (2014) A framework for suspicious object detection from surveillance video. Int J Mach Intel Sensory Signal Process 1(3):251–266

    Google Scholar 

  • 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 1–4

  • 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

  • Trecvid 2010 evaluation for surveillance detection (2010). http://www.itl.nist.gov/iad/mig/tests/trecvid/2010/

  • Vezzani R, Cucchiara R (2010) Video surveillance online repository (visor): an integrated framework. Multimed Tools Appl 50(2):359–380

    Article  Google Scholar 

  • Vicente J, Guillemant P (2002) An image processing technique for automatically detecting forest fire. Int J Therm Sci 41(12):1113–1120

    Article  Google Scholar 

  • Vu VT, Brémond F, Thonnat M (2002) Temporal constraints for video interpretation. In 15th European conference on artificial intelligence

  • 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):539–555

    Article  Google Scholar 

  • 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):11603–11613

  • Wieser D, Brupbacher T (2001) Smoke detection in tunnels using video images. NIST SPECIAL PUBLICATION SP, pp 79–90

  • 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):194–209

    Article  Google Scholar 

  • 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, POCA

  • 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):780–785

    Article  Google Scholar 

  • 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 380–386

  • Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13

    Article  Google Scholar 

  • Yogameena B, Deepila G, Mehjabeen J (2012) RVM based human fall analysis for video surveillance applications? Res J Appl Sci Eng Technol 4(24):5361–5366

    Google Scholar 

  • 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):1274–1286

    Article  Google Scholar 

  • Yuan F (2008) A fast accumulative motion orientation model based on integral image for video smoke detection. Pattern Recogn Lett 29(7):925–932

    Article  Google Scholar 

  • Yuan F (2010) An integrated fire detection and suppression system based on widely available video surveillance. Mach Vis Appl 21(6):941–948

    Article  Google Scholar 

  • 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 21–26

  • 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):1489–1498

    Article  Google Scholar 

  • Ziaeefard M, Bergevin R (2015) Semantic human activity recognition: a literature review. Pattern Recogn 8(48):2329–2345

    Article  Google Scholar 

  • 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):295–306

    Google Scholar 

  • 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 2

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Authors and Affiliations

  1. Department of CEA, IET, GLA University, Mathura, Uttar Pradesh, India

    Rajesh Kumar Tripathi, Anand Singh Jalal & Subhash Chand Agrawal

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  1. Rajesh Kumar Tripathi
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  2. Anand Singh Jalal
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  3. Subhash Chand Agrawal
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Correspondence to Anand Singh Jalal.

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Tripathi, R.K., Jalal, A.S. & Agrawal, S.C. Suspicious human activity recognition: a review. Artif Intell Rev 50, 283–339 (2018). https://doi.org/10.1007/s10462-017-9545-7

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