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International Journal of Multimedia Information Retrieval
Erschienen in: International Journal of Multimedia Information Retrieval 2/2022

29.03.2022 | Trends and Surveys

Anomaly detection using edge computing in video surveillance system: review

verfasst von: Devashree R. Patrikar, Mayur Rajaram Parate

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 2/2022

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Abstract

The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.
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Literatur
1.
Ahmed SA, Dogra DP, Kar S, Roy PP (2018) Trajectory-based surveillance analysis: a survey. IEEE Trans Circuits Syst Video Technol 29(7):1985–1997CrossRef
2.
Ajay B, Rao M (2021) Binary neural network based real time emotion detection on an edge computing device to detect passenger anomaly, In: 2021 34th International conference on VLSI design and 2021 20th international conference on embedded systems (VLSID) pp 175–180, IEEE
3.
Angelini F, Yan J, Naqvi SM (2019) Privacy-preserving online human behaviour anomaly detection based on body movements and objects positions. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP) pp 8444–8448, IEEE
4.
Asad M, Yang J, He J, Shamsolmoali P, He X (2021) Multi-frame feature-fusion-based model for violence detection. Visual Comput 37(6):1415–1431CrossRef
5.
Bansod SD, Nandedkar AV (2020) Crowd anomaly detection and localization using histogram of magnitude and momentum. Vis Comput 36(3):609–620CrossRef
6.
7.
Bock F, Di Martino S, Origlia A (2019) Smart parking: Using a crowd of taxis to sense on-street parking space availability. IEEE Transactions on Intelligent Transportation Systems 21(2):496–508CrossRef
8.
Bonetto M, Korshunov P, Ramponi G, Ebrahimi T (2015) Privacy in mini-drone based video surveillance. In 2015 11th IEEE international conference and workshops on automatic face and gesture recognition (FG) IEEE vol 4, pp 1–6
9.
Bozcan I, Kayacan E (2021) Context-dependent anomaly detection for low altitude traffic surveillance, arXiv preprint arXiv:​2104.​06781
10.
Chakravarthy AS, Sinha S, Narang P, Mandal M Dronesegnet: robust aerial semantic segmentation for uav-based iot applications
11.
12.
Cheng K-W, Chen Y-T, Fang W-H (2015) Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. IEEE Trans Image Process 24(12):5288–5301MathSciNetMATHCrossRef
13.
Cheng H, Liu X, Wang H, Fang Y, Wang M, Zhao X (2020) Securead: a secure video anomaly detection framework on convolutional neural network in edge computing environment. IEEE Trans Cloud Comput
14.
Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Xiao T, Xu B, Zhang C, Zhang Z (2015) Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:​1512.​01274
15.
Choi K, Yi J, Park C, Yoon S (2021) Deep learning for anomaly detection in time-series data: review, analysis, and guidelines. IEEE Access 9:120043–120065CrossRef
16.
Chowdhury SS, Islam KM, Noor R (2020) Anomaly detection in unsupervised surveillance setting using ensemble of multimodal data with adversarial defense. arXiv preprint arXiv:​2007.​10812
17.
Chowdhury SS, Islam KM, Noor R (2020) Unsupervised abnormality detection using heterogeneous autonomous systems. arXiv preprint arXiv:​2006.​03733
18.
Chriki A, Touati H, Snoussi H, Kamoun F (2021) Deep learning and handcrafted features for one-class anomaly detection in uav video. Multimed Tools Appl 80(2):2599–2620CrossRef
19.
Chriki A, Touati H, Snoussi H, Kamoun F (2020) Uav-based surveillance system: an anomaly detection approach. In: 2020 IEEE Symposium on computers and communications (ISCC)
20.
Chu W, Xue H, Yao C, Cai D (2018) Sparse coding guided spatiotemporal feature learning for abnormal event detection in large videos. IEEE Trans Multimed 21(1):246–255CrossRef
21.
Colque RVHM, Caetano C, de Andrade MTL, Schwartz WR (2016) Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Trans Circuits Syst Video Technol 27(3):673–682CrossRef
22.
Coşar S, Donatiello G, Bogorny V, Garate C, Alvares LO, Brémond F (2016) Toward abnormal trajectory and event detection in video surveillance. IEEE Trans Circuits Syst Video Technol 27(3):683–695CrossRef
23.
Deng X, Liu Y, Zhu C, Zhang H (2021) Air-ground surveillance sensor network based on edge computing for target tracking. Comput Commun 166:254–261CrossRef
24.
Dinh TQ, La QD, Quek TQ, Shin H (2018) Learning for computation offloading in mobile edge computing. IEEE Trans Commun 66(12):6353–6367CrossRef
25.
Dong F, Zhang Y, Nie X (2020) Dual discriminator generative adversarial network for video anomaly detection. IEEE Access 8:88170–88176CrossRef
26.
Du L, Du Y, Li Y, Su J, Kuan Y-C, Liu C-C, Chang M-CF (2017) A reconfigurable streaming deep convolutional neural network accelerator for internet of things. IEEE Trans Circuits Syst I Regul Pap 65(1):198–208CrossRef
27.
Elgarrai Z, El Meslouhi O, Kardouchi M, Allali H (2016) Robust facial expression recognition system based on hidden markov models. Int J Multimed Inf Retr 5(4):229–236CrossRef
28.
Ergen T, Kozat SS (2019) Unsupervised anomaly detection with lstm neural networks. IEEE Trans Neural Netw Learning Syst 31(8):3127–3141MathSciNetCrossRef
29.
Farooq MU, Khan NA, Ali MS (2017) Unsupervised video surveillance for anomaly detection of street traffic. Int J Adv Comput Sci Appl(IJACSA) 12(8):270–275
30.
31.
Georgiou T, Liu Y, Chen W, Lew M (2020) A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision. Int J Multimed Inf Retr 9(3):135–170CrossRef
32.
Ghosh AM, Grolinger K (2020) Edge-cloud computing for internet of things data analytics: embedding intelligence in the edge with deep learning. IEEE Trans Ind Inf 17(3):2191–2200
33.
Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, JMLR Workshop and conference proceedings, pp 315–323. http://​proceedings.​mlr.​press/​v15/​glorot11a
34.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst vol 27
35.
Guo D, Li W, Fang X (2018) Fully convolutional network for multiscale temporal action proposals. IEEE Trans Multimed 20(12):3428–3438CrossRef
36.
Guo F, Wang Z, Du S, Li H, Zhu H, Pei Q, Cao Z, Zhao J (2019) Detecting vehicle anomaly in the edge via sensor consistency and frequency characteristic. IEEE Trans Veh Technol 68(6):5618–5628CrossRef
37.
Hamdi S, Bouindour S, Snoussi H, Wang T, Abid M (2021) End-to-end deep one-class learning for anomaly detection in uav video stream. J Imaging 7(5):90CrossRef
38.
39.
Hou J, Wu X, Sun Y, Jia Y (2017) Content-attention representation by factorized action-scene network for action recognition. IEEE Trans Multimed 20(6):1537–1547CrossRef
40.
Hu P, Ning H, Qiu T, Zhang Y, Luo X (2016) Fog computing based face identification and resolution scheme in internet of things. IEEE Trans Ind Inf 13(4):1910–1920CrossRef
41.
Hu W, Gao J, Li B, Wu O, Du J, Maybank S (2018) Anomaly detection using local kernel density estimation and context-based regression. IEEE Trans Knowl Data Eng 32(2):218–233CrossRef
42.
43.
Ji S, Xu W, Yang M, Yu K (2012) 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231CrossRef
44.
Jiang T, Li Y, Xie W, Du Q (2020) Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection. IEEE Trans Geosci Rem Sens 58(7):4666–4679CrossRef
45.
Kaltsa V, Briassouli A, Kompatsiaris I, Hadjileontiadis LJ, Strintzis MG (2015) Swarm intelligence for detecting interesting events in crowded environments. IEEE Tran Image Process 24(7):2153–2166MathSciNetMATHCrossRef
46.
47.
Ke R, Li Z, Kim S, Ash J, Cui Z, Wang Y (2016) Real-time bidirectional traffic flow parameter estimation from aerial videos. IEEE Transactions on Intelligent Transportation Systems 18(4):890–901CrossRef
48.
Ke R, Li Z, Tang J, Pan Z, Wang Y (2018) Real-time traffic flow parameter estimation from uav video based on ensemble classifier and optical flow. IEEE Trans Intell Transp Syst 20(1):54–64CrossRef
49.
Ke R, Zhuang Y, Pu Z, Wang Y (2020) A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on IoT devices. IEEE Trans Intell Transp Syst 22(8):4962–4974CrossRef
50.
Khan SD, Basalamah S (2020) Scale and density invariant head detection deep model for crowd counting in pedestrian crowds. Visual Comput 37(8):2127–37CrossRef
51.
Kim W-J, Youn C-H (2020) Lightweight online profiling-based configuration adaptation for video analytics system in edge computing. IEEE Access 8:116881–116899CrossRef
52.
Kim J-H, Kim N, Won CS (2021) Deep edge computing for videos. IEEE Access 9:123348–123357CrossRef
53.
Kong X, Wang K, Wang S, Wang X, Jiang X, Guo Y, Shen G, Chen X, Ni Q (2021) Real-time mask identification for covid-19: an edge computing-based deep learning framework. IEEE Internet Things J 8(21):15929–38CrossRef
54.
Kumar MP, Jayagopal P (2020) Generative adversarial networks: a survey on applications and challenges. Int J Multimed Inf Retr 10(1):1–24
55.
Kumar D, Bezdek JC, Rajasegarar S, Leckie C, Palaniswami M (2017) A visual-numeric approach to clustering and anomaly detection for trajectory data. Visual Comput 33(3):265–281CrossRef
56.
Kumaran SK, Dogra SK, Roy PP (2019) Anomaly detection in road traffic using visual surveillance: A survey, arXiv preprint arXiv:​1901.​08292
57.
Lai C-F, Chien W-C, Yang LT, Qiang W (2019) Lstm and edge computing for big data feature recognition of industrial electrical equipment. IEEE Trans Ind Inf 15(4):2469–2477CrossRef
58.
Lamba S, Nain N (2019) Segmentation of crowd flow by trajectory clustering in active contours. Vis Comput pp 1–12
59.
Lao W, Han J, De With PH (2009) Automatic video-based human motion analyzer for consumer surveillance system. IEEE Transactions on Consumer Electronics 55(2):591–598CrossRef
60.
Lee SW, Kim YS, Bien Z (2009) A nonsupervised learning framework of human behavior patterns based on sequential actions. IEEE Transactions on Knowledge and Data Engineering 22(4):479–492CrossRef
61.
Lei J, Fang S, Xie W, Li Y, Chang C-I (2020) Discriminative reconstruction for hyperspectral anomaly detection with spectral learning. IEEE Trans Geosci Rem Sens 58(10):7406–7417CrossRef
62.
Li W, Mahadevan V, Vasconcelos N (2013) Anomaly detection and localization in crowded scenes. IEEE transactions on pattern analysis and machine intelligence 36(1):18–32
63.
Li T, Chang H, Wang M, Ni B, Hong R, Yan S (2014) Crowded scene analysis:a survey. IEEE Trans Circuits Syst Video Technol 25(3):367–386CrossRef
64.
Li Y, Guo T, Xia R, Xie W (2018) Road traffic anomaly detection based on fuzzy theory. IEEE Access 6:40281–40288CrossRef
65.
Li L, Ota K, Dong M (2018) Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans Ind Inf 14(10):4665–4673CrossRef
66.
Li L, Wang Z, Hu Q, Dong Y (2020) Adaptive non-convex sparsity based background subtraction for intelligent video surveillance. IEEE Trans Ind Inf 17(6):4168–78CrossRef
67.
Li Z, Li Y, Gao Z (2020) Spatiotemporal representation learning for video anomaly detection. IEEE Access 8:25531–25542CrossRef
68.
Li X, Cai Z-m (2016) Anomaly detection techniques in surveillance videos. In: 2016 9th International congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI), pp. 54–59, IEEE
69.
Liu C, Chang F (2018) Hybrid cascade structure for license plate detection in large visual surveillance scenes. IEEE Trans Intell Transp Syst 20(6):2122–2135CrossRef
70.
Liu P, Yang P, Wang C, Huang K, Tan T (2016) A semi-supervised method for surveillance-based visual location recognition. IEEE Trans Cybern 47(11):3719–3732CrossRef
71.
Liu SW, Ngan HY, Ng MK, Simske SJ (2018) Accumulated relative density outlier detection for large scale traffic data. Electron Imaging 2018(9):1–239
72.
Liu J, Xia Y, Tang Z (2021) Privacy-preserving video fall detection using visual shielding information. The Visual Computer 37(2):359–370CrossRef
73.
Liu W, Luo W, Lian D, Gao S (2018) Future frame prediction for anomaly detection–a new baseline. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6536–6545
74.
Long C, Cao Y, Jiang T, Zhang Q (2017) Edge computing framework for cooperative video processing in multimedia iot systems. IEEE Trans Multimed 20(5):1126–1139CrossRef
75.
Luo W, Liu W, Lian D, Tang J, Duan L, Peng X, Gao S (2019) Video anomaly detection with sparse coding inspired deep neural networks. IEEE Trans Pattern Anal Mach Intell 43(3):1070–1084CrossRef
76.
Luo W, Liu W, Gao S (2017) A revisit of sparse coding based anomaly detection in stacked rnn framework. In: Proceedings of the IEEE international conference on computer vision pp. 341–349
77.
Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE international conference on computer vision pp. 2720–2727
78.
Ma X, Wang S, Zhang S, Yang P, Lin C, Shen XS (2019) Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing. IEEE Trans Cloud Comput 9(3):968–980CrossRef
79.
Mahadevan V, Li W, Bhalodia V, Vasconcelos N (2010) Anomaly detection in crowded scenes. In 2010 IEEE Computer society conference on computer vision and pattern recognition, IEEE pp. 1975–1981
80.
Mo X, Monga V, Bala R, Fan Z (2013) Adaptive sparse representations for video anomaly detection. IEEE Trans Circuits Syst Video Technol 24(4):631–645
81.
Muhammad K, Ahmad J, Lv Z, Bellavista P, Yang P, Baik SW (2018) Efficient deep cnn-based fire detection and localization in video surveillance applications. IEEE Trans Syst Man Cybern Syst 49(7):1419–1434CrossRef
82.
Muhammad K, Khan S, Elhoseny M, Ahmed SH, Baik SW (2019) Efficient fire detection for uncertain surveillance environment. IEEE Trans Ind Inf 15(5):3113–3122CrossRef
83.
Muhammad K, Khan S, Palade V, Mehmood I, De Albuquerque VHC (2019) Edge intelligence-assisted smoke detection in foggy surveillance environments. IEEE Trans Ind Inf 16(2):1067–1075CrossRef
84.
Nawaratne R, Alahakoon D, De Silva D, Yu X (2019) Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Trans Ind Inf 16(1):393–402CrossRef
85.
Nayak R, Pati UC, Das SK (2021) A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing 106:104078CrossRef
86.
Nguyen V, Phung D, Pham D-S, Venkatesh S (2015) Bayesian nonparametric approaches to abnormality detection in video surveillance. Ann Data Sci 2(1):21–41CrossRef
87.
Nieto RM, García-Martín Á, Hauptmann AG, Martínez JM (2018) Automatic vacant parking places management system using multicamera vehicle detection. IEEE Trans Intell Transp Syst 20(3):1069–1080CrossRef
88.
Nikouei SY, Chen Y, Song S, Xu R, Choi B-Y, Faughnan T (2018) Smart surveillance as an edge network service: srom harr-cascade, svm to a lightweight cnn. In: 2018 ieee 4th international conference on collaboration and internet computing (cic) pp. 256–265, IEEE
89.
Nikouei SY, Chen Y, Song S, Choi B-Y, Faughnan TR (2019) Toward intelligent surveillance as an edge network service (isense) using lightweight detection and tracking algorithms. IEEE Trans Serv Comput. 14(6):1624–37CrossRef
90.
Oza P, Patel VM (2018) One-class convolutional neural network. IEEE Signal Process Lett 26(2):277–281CrossRef
91.
Parate MR, Bhurchandi KM, Kothari AG (2021) Anomaly detection in residential video surveillance on edge devices in iot framework. arXiv preprint arXiv:​2107.​04767
92.
Parate MR, Bhurchandi KM (2017) Structurally enhanced correlation tracking. KSII Trans Internet & Inf Syst 11(10):4929–4947
93.
Parate MR, Satpute VR, Bhurchandi KM (2018) Global-patch-hybrid template-based arbitrary object tracking with integral channel features. Appl Intell 48(2):300–314CrossRef
94.
95.
Perveen N, Roy D, Mohan CK (2018) Spontaneous expression recognition using universal attribute model. IEEE Trans Image Process 27(11):5575–5584MathSciNetCrossRef
96.
Popoola OP, Wang K (2012) Video-based abnormal human behavior recognition-a review. IEEE Trans Syst Man Cybern. Part C (Appl Rev) 42(6):865–878
97.
Puvvadi UL, Di Benedetto K, Patil A, Kang K-D, Park Y (2015) Cost-effective security support in real-time video surveillance. IEEE Transactions on Industrial Informatics 11(6):1457–1465CrossRef
98.
Qiang Y, Fei S, Jiao Y (2021) Anomaly detection based on latent feature training in surveillance scenarios. IEEE Access 9:68108–68117CrossRef
99.
100.
Ren W, Li G, Sun B, Huang K (2015) Unsupervised kernel learning for abnormal events detection. Vis Comput 31(3):245–255CrossRef
101.
102.
Ristea N-C, Madan N, Ionescu RT, Nasrollahi K, Khan FS, Moeslund TB, Shah M (2021) Self-supervised predictive convolutional attentive block for anomaly detection. arXiv preprint arXiv:​2111.​09099
103.
Rodrigues TG, Suto K, Nishiyama H, Kato N (2016) Hybrid method for minimizing service delay in edge cloud computing through vm migration and transmission power control. IEEE Trans Comput 66(5):810–819MathSciNetCrossRef
104.
Roy D, Murty KSR, Mohan CK (2018) Unsupervised universal attribute modeling for action recognition. IEEE Trans Multimed 21(7):1672–1680CrossRef
105.
106.
Sabih M, Vishwakarma DK (2021) Crowd anomaly detection with lstms using optical features and domain knowledge for improved inferring. The Visual Computer, pp 1–12
107.
Sabokrou M, Fayyaz M, Fathy M, Klette R (2017) Deep-cascade: cscading 3d deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans Image Process 26(4):1992–2004MathSciNetMATHCrossRef
108.
Sabokrou M, Fayyaz M, Fathy M, Moayed Z, Klette R (2018) Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput Vis Image Understanding 172:88–97MATHCrossRef
109.
Sabokrou M, Fathy M, Zhao G, Adeli E (2020) Deep end-to-end one-class classifier. IEEE Trans Neural Netw Learning Syst 32(2):675–684CrossRef
110.
Saligrama V, Chen Z (2012) Video anomaly detection based on local statistical aggregates. In 2012 IEEE Conference on computer vision and pattern recognition pp 2112–2119, IEEE,
111.
Samuel DJ, Cuzzolin F (2021) Svd-gan for real-time unsupervised video anomaly detection
112.
Sarker MI, Losada-Gutiérrez C, Marrón-Romera M, Fuentes-Jiménez D, Luengo-Sánchez S (2021) Semi-supervised anomaly detection in video-surveillance scenes in the wild. Sensors 21(12):3993CrossRef
113.
Saypadith S, Onoye T (2021) An approach to detect anomaly in video using deep generative network. IEEE Access 9:150903–150910CrossRef
114.
Schneible J, Lu A (2017) Anomaly detection on the edge. In MILCOM 2017-2017 IEEE Military communications conference (MILCOM),IEEE pp 678–682
115.
Schneible J, Lu A (2017) Anomaly detection on the edge. In: MILCOM 2017 - 2017 IEEE Military communications conference (MILCOM), pp 678–682
116.
117.
Shi Y, Tian Y, Wang Y, Huang T (2017) Sequential deep trajectory descriptor for action recognition with three-stream cnn. IEEE Trans Multimed 19(7):1510–1520CrossRef
118.
Shirahama K, Grzegorzek M, Uehara K (2015) Weakly supervised detection of video events using hidden conditional random fields. Int J Multimed Inf Retr 4(1):17–32CrossRef
119.
Shobha B, Deepu R (2018) A review on video based vehicle detection, recognition and tracking. In: 2018 3rd International conference on computational systems and information technology for sustainable solutions (CSITSS) pp 183–186, IEEE
120.
Shojaei G, Razzazi F (2019) Semi-supervised domain adaptation for pedestrian detection in video surveillance based on maximum independence assumption. Int J Multimed Inf Retr 8(4):241–252CrossRef
121.
Singh D, Mohan CK (2017) Graph formulation of video activities for abnormal activity recognition. Pattern Recognit 65:265–272CrossRef
122.
123.
Sivaraman S, Trivedi MM (2013) Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans Intell Transp Syst 14(4):1773–1795CrossRef
124.
Sodemann AA, Ross MP, Borghetti BJ (2012) A review of anomaly detection in automated surveillance. In: IEEE Trans Syst Man Cybern, Part C (Applications and Reviews) 42(6):1257–1272
125.
Song H, Sun C, Wu X, Chen M, Jia Y (2019) Learning normal patterns via adversarial attention-based autoencoder for abnormal event detection in videos. IEEE Trans Multimed 22(8):2138–2148CrossRef
126.
Srivastava S, Singh SP (2016) A survey on latency reduction approaches for performance optimization in cloud computing. In: 2016 Second International conference on computational intelligence & communication technology (CICT), pp 111–115, IEEE
127.
Sun H, Yu Y, Sha K, Lou B (2019) mvideo: edge computing based mobile video processing systems. IEEE Access 8:11615–11623CrossRef
128.
Sun C, Jia Y, Song H, Wu Y (2020) Adversarial 3d convolutional auto-encoder for abnormal event detection in videos. IEEE Trans Multimed 23:3292–305CrossRef
129.
Suresha M, Kuppa S, Raghukumar D (2020) A study on deep learning spatiotemporal models and feature extraction techniques for video understanding. Int J Multimed Inf Retr 9(2):81–101CrossRef
130.
Suto K, Miyanabe K, Nishiyama H, Kato N, Ujikawa H, Suzuki K-I (2015) Qoe-guaranteed and power-efficient network operation for cloud radio access network with power over fiber. IEEE Trans Comput Soc Syst 2(4):127–136CrossRef
131.
Sze V, Chen Y, Yang T, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105(12):2295–2329CrossRef
132.
Tariq S, Farooq H, Jaleel A, Wasif SM et al (2021) Anomaly detection with particle filtering for online video surveillance. IEEE Access 9:19457–19468CrossRef
133.
Tian B, Morris BT, Tang M, Liu Y, Yao Y, Gou C, Shen D, Tang S (2014) Hierarchical and networked vehicle surveillance in its: a survey. IEEE Trans Intell Transp Syst 16(2):557–580CrossRef
134.
Torres BS, Pedrini H (2018) Detection of complex video events through visual rhythm. The Visual Computer 34(2):145–165CrossRef
135.
Tripathi G, Singh K, Vishwakarma DK (2019) Convolutional neural networks for crowd behaviour analysis: a survey. Vis Comput 35(5):753–776CrossRef
136.
Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2017) Action recognition in video sequences using deep bi-directional lstm with cnn features. IEEE Access 6:1155–1166CrossRef
137.
Ullah W, Ullah A, Hussain T, Muhammad K, Heidari AA, Del Ser J, Baik SW, De Albuquerque VHC (2021) Artificial intelligence of things-assisted two-stream neural network for anomaly detection in surveillance big video data. Future Gener Comput Syst 129:286–297CrossRef
138.
Ullah W, Ullah A, Hussain T, Khan ZA, Baik SW (2021) An efficient anomaly recognition framework using an attention residual lstm in surveillance videos. Sensors 21(8):2811CrossRef
139.
Ullah W, Ullah A, Haq IU, Muhammad K, Sajjad M, Baik SW (2021) Cnn features with bi-directional lstm for real-time anomaly detection in surveillance networks. Multimed Tools Appl 80(11):16979–16995CrossRef
140.
Varghese B, Wang N, Barbhuiya S, Kilpatrick P, Nikolopoulos DS (2016) Challenges and opportunities in edge computing. In 2016 IEEE International conference on smart cloud (SmartCloud), IEEE pp. 20–26
141.
Vosta S, Yow K-C (2022) A cnn-rnn combined structure for real-world violence detection in surveillance cameras. Appl Sci 12(3):1021CrossRef
142.
Wan S, Ding S, Chen C (2022) Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles. Pattern Recognit 121:108146CrossRef
143.
Wang T, Snoussi H (2014) Detection of abnormal visual events via global optical flow orientation histogram. IEEE Trans Inf Forensics Secur 9(6):988–998CrossRef
144.
Wang X, Ning Z, Wang L (2018) Offloading in internet of vehicles: A fog-enabled real-time traffic management system. IEEE Transactions on Industrial Informatics 14(10):4568–4578CrossRef
145.
Wang Y, Wang K, Huang H, Miyazaki T, Guo S (2018) Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications. IEEE Trans Ind Inf 15(2):976–986CrossRef
146.
Wang T, Qiao M, Zhu A, Niu Y, Li C, Snoussi H (2018) Abnormal event detection via covariance matrix for optical flow based feature. Multimed Tools Appl 77(13):17375–17395
147.
Wang C, Dong S, Zhao X, Papanastasiou G, Zhang H, Yang G (2019) Saliencygan: deep learning semisupervised salient object detection in the fog of iot. IEEE Trans Ind Inf 16(4):2667–2676CrossRef
148.
Wu P, Liu J, Shen F (2019) A deep one-class neural network for anomalous event detection in complex scenes. IEEE Trans Neural Netw Learning Syst 31(7):2609–2622
149.
Xu K, Jiang X, Sun T (2018) Anomaly detection based on stacked sparse coding with intraframe classification strategy. IEEE Trans Multimed 20(5):1062–1074CrossRef
150.
Xu Y, Du B, Zhang L, Chang S (2019) A low-rank and sparse matrix decomposition-based dictionary reconstruction and anomaly extraction framework for hyperspectral anomaly detection. IEEE Geosci Remote Sens Lett 17(7):1248–1252CrossRef
151.
Xu X, Wu Q, Qi L, Dou W, Tsai S-B, Bhuiyan MZA (2020) Trust-aware service offloading for video surveillance in edge computing enabled internet of vehicles. IEEE Trans Intell Trans Syst 22(3):1787–1796CrossRef
152.
Xu Z, Li J, Zhang M (2021) A surveillance video real-time analysis system based on edge-cloud and fl-yolo cooperation in coal mine. IEEE Access 9:68482–68497CrossRef
153.
Xu R, Nikouei SY, Chen Y, Polunchenko A, Song S, Deng C, Faughnan TR (2018) Real-time human objects tracking for smart surveillance at the edge. In: 2018 IEEE International conference on communications (ICC), pp. 1–6, IEEE
154.
Yang L, Cao J, Liang G, Han X (2015) Cost aware service placement and load dispatching in mobile cloud systems. IEEE Trans Comput 65(5):1440–1452MathSciNetMATHCrossRef
155.
Yang P, Lyu F, Wu W, Zhang N, Yu L, Shen XS (2019) Edge coordinated query configuration for low-latency and accurate video analytics. IEEE Trans Ind Inf 16(7):4855–4864CrossRef
156.
Yang Z, Liu J, Wu P (2021) Bidirectional retrospective generation adversarial network for anomaly detection in videos. IEEE Access 9:107842–107857CrossRef
157.
Yu B, Liu Y, Sun Q (2016) A content-adaptively sparse reconstruction method for abnormal events detection with low-rank property. IEEE Trans Syst Man Cybern Syst 47(4):704–716CrossRef
158.
Yuan Y, Fang J, Wang Q (2014) Online anomaly detection in crowd scenes via structure analysis. IEEE Trans Cybern 45(3):548–561CrossRef
159.
Yuan G, Sun P, Zhao J, Li D, Wang C (2017) A review of moving object trajectory clustering algorithms. Artif Intell Rev 47(1):123–144CrossRef
160.
161.
Zhai X, Liu K, Nash W, Castineira D (2020) Smart autopilot drone system for surface surveillance and anomaly detection via customizable deep neural network. In: International petroleum technology conference, OnePetro
162.
Zhang Z, Mei X, Xiao B (2015) Abnormal event detection via compact low-rank sparse learning. IEEE Intell Syst 31(2):29–36CrossRef
163.
Zhang Q, Yang LT, Yan Z, Chen Z, Li P (2018) An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans Ind Inf 14(7):3170–3178CrossRef
164.
Zhang J, Xu C, Gao Z, Rodrigues JJ, Albuquerque V (2020) Industrial pervasive edge computing-based intelligence iot for surveillance saliency detection. IEEE Transactions on Industrial Informatics 17(7):5012–5020CrossRef
165.
Zhang W, Wang G, Huang M, Wang H, Wen S (2021) Generative adversarial networks for abnormal event detection in videos based on self-attention mechanism. IEEE Access 9:124847–124860
166.
Zhang Y, Zhou D, Chen S, Gao S, Ma Y (2016) Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 589–597
167.
Zhao Y, Yin Y, Gui G (2020) Lightweight deep learning based intelligent edge surveillance techniques. IEEE Trans Cogn Commun Netw 6(4):1146–1154CrossRef
168.
Zheng X, Zhang Y, Zheng Y, Luo F, Lu X (2021) Abnormal event detection by a weakly supervised temporal attention network. CAAI Trans Intell Technol
169.
Zhou Y, Liu L, Shao L, Mellor M (2017) Fast automatic vehicle annotation for urban traffic surveillance. IEEE Trans Intell Transp Syst 19(6):1973-1984CrossRef
170.
Zhou JT, Du J, Zhu H, Peng X, Liu Y, Goh RSM (2019) Anomalynet: an anomaly detection network for video surveillance. IEEE Trans Inf Forensics Secur 14(10):2537–2550CrossRef
Metadaten
Titel
Anomaly detection using edge computing in video surveillance system: review
verfasst von
Devashree R. Patrikar
Mayur Rajaram Parate
Publikationsdatum
29.03.2022
Verlag
Springer London
Erschienen in
International Journal of Multimedia Information Retrieval / Ausgabe 2/2022
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-022-00227-8

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