Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Building a Language Data Set in Telugu Using Machine Learning Techniques to Address Suicidal Ideation and Behaviors in Adolescents Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Anomaly Detection in Videos Using Deep Learning Techniques

Authors : Akshaya Ravichandran, Suresh Sankaranarayanan

Published in: Applications of Artificial Intelligence and Machine Learning

Publisher: Springer Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

People’s concern for safety in public places has been increasing nowadays and anomaly detection in crowded places has become very important due to this aspect. This paper provides an approach towards identifying suspicious behaviors automatically in a crowded environment. In light of this, we have validated two powerful deep learning based models namely CNN and VGG16. So, for this purpose, we have collected a number of CCTV videos for detecting and differentiating between both normal and anomalous activities. Now based on videos collected, they have been trained using VGG16 and CNN model towards achieving the best accuracy

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Air Quality Prediction Using Regression Models
next chapter Unsupervised Activity Modelling in a Video
Literature
1.
Tan H, Zhai Y, Liu Y, Zhang M (2016) Fast anomaly detection in traffic surveillance video based on robust sparse optical flow. In: 2016 IEEE international conference on acoustics speech and signal processing (ICASSP), pp 1976–1980, Mar 2016
2.
Hasan M, Choi J, Neumann J, Roy-Chowdhury AK, Davis LS (2016) Learning temporal regularity in video sequences. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 733–742, June 2016
3.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef
4.
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the 27th international conference on neural information processing systems, vol 2, Ser NIPS’14. MIT Press, Cambridge, MA, USA, pp 3104–3112
5.
Shi X, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Proceedings of the 28th international conference on neural information processing systems, vol 1, Ser NIPS’15. MIT Press, Cambridge, MA, USA, pp 802–810
6.
Zeiler MD, Krishnan D, Taylor GW, Fergus R (2010) Deconvolutional networks. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2528–2535, June 2010
7.
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR. abs/1412.6980
8.
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, June 2012
9.
Xu D, Ricci E, Yan Y, Song J, Sebe N (2015) Learning deep representations of appearance and motion for anomalous event detection, BMVC. BMVA Press, pp 8.1–8.12
10.
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, pp 1975–1981, June 2010
11.
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv
12.
Ramchandran A, Kumar Sangaiah A (2018) Unsupervised anomaly detection for high-dimensional data-exploratory data analysis. In: Computational intelligence for multimedia big data on the cloud with engineering applications
13.
Zhou S, Shen W, Zeng D, Fang M, Wei Y, Zhang Z (2016) Spatial-temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Process Image Commun 47(C):358–368
14.
Piciarelli C, Micheloni C, Foresti GL (2008) Trajectory-based anomalous event detection. IEEE Trans Circuits Syst Video Technol 18(11):1544–1554CrossRef
15.
Li F, Yang W, Liao Q (2016) An efficient anomaly detection approach in surveillance video based on oriented GMM. In: 2016 IEEE international conference on acoustics speech and signal processing (ICASSP), pp 1981–1985, Mar 2016
16.
Tang X, Zhang S, Yao H (2013) Sparse coding based motion attention for abnormal event detection. In: 2013 IEEE international conference on image processing, pp 3602–3606, Sept 2013
17.
Kim J, Grauman K (2009) Observe locally infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: 2009 IEEE conference on computer vision and pattern recognition, pp 2921–2928, June 2009
18.
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, vol 25. Curran Associates Inc. pp 1097–1105
19.
Wang L, Qiao Y, Tang X (2015) Action recognition with trajectory-pooled deep-convolutional descriptors. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 4305–4314, June 2015
20.
21.
22.
Metadata
Title
Anomaly Detection in Videos Using Deep Learning Techniques
Authors
Akshaya Ravichandran
Suresh Sankaranarayanan
Copyright Year
2021
Publisher
Springer Singapore
Book
Applications of Artificial Intelligence and Machine Learning

Print ISBN: 978-981-16-3066-8

Electronic ISBN: 978-981-16-3067-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-3067-5_20

Premium Partner

    Image Credits