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Cited by 26Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsAdvanced Image and Video Retrieval Techniques

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Improved Object Detection in Video Surveillance Using Deep Convolutional Neural Network Learning

B, Dhiyanesh, K, Rajesh Kanna, S, Rajkumar, R, Radha·2021·2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
DOI 10.1109/i-smac52330.2021.9640894
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Abstract

In this paper, a convolutional neural network is used to improve the functionality of object detection using probabilistic neural network in the analysis of surveillance images. Next, this research work attempts to examine the fundamental principles and some of the underlying theories of perceptual and neural networks are often missed. This helps you understand why, in many areas of usage, deep learning has increased. The processing of surveillance photos is obviously one of the areas most influenced by this exponential progress, particularly in the identification and detection of images. The simulation result shows that the CNN-PNN obtained improved achievements in simulation for the optimal detection of objects in video streaming or video surveillance systems. The results show that the proposed method achieves higher rate of accuracy, f-measure and reduced percentage error than other methods.

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Crossref · 10.1109/i-smac52330.2021.9640894

Cites

3 of 3 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 3 references

Research works printed but not linked: 22

  1. 10.1109/ICISCE.2017.156 · DOI
  2. 10.1109/TPAMI.2021.3051099 · DOI
  3. kasthuripriya, 2016, LFTSM-Local Flow Trust Based Service Monitoring Approach for Preventing the Packet During Data Transfer in Cloud, Asian Journal of Information Technology, 15, 3927
  4. 10.1016/j.neucom.2020.01.085 · DOI · Paper page
  5. 10.1016/j.neucom.2020.01.085 · DOI · Paper page
  6. dhiyanesh, 2015, Secure Data Storage Auditing Service Using Third Party Auditor In Cloud Computing, International Journal of Applied Engineering Research, 10
  7. zhao, 2019, Object detection with deep learning: A review, IEEE Transactions on Neural Networks and Learning Systems, 30, 3212, 10.1109/TNNLS.2018.2876865 · DOI · Paper page
  8. 10.1109/ICIS.2017.7960069 · DOI
  9. karthikeyan, 2011, Location Based Scheduler for Independent Jobs in Computational Grids, CiiT International Journal of Networking and Communication Engineering, 3, 243
  10. 10.1109/CVPR.2014.81 · DOI · Paper page
  11. dhiyanesh, 2020, Threshold based DDoS mitigation with fog layer in cloud environment, Journal of Ambient Intelligence and Humanized Computing
  12. 10.1007/978-3-319-70353-4_13 · DOI
  13. 10.1109/CRV.2018.00052 · DOI
  14. 10.1016/j.procs.2018.05.144 · DOI
  15. 10.1109/MSP.2017.2749125 · DOI
  16. 10.1166/jctn.2016.5766 · DOI
  17. 10.1109/ICIRCA.2018.8597266 · DOI
  18. girshick, 2015, Fast r-cnn, Proceedings of the IEEE International Conference on Computer Vision, 1440
  19. 10.21629/JSEE.2018.06.17 · DOI
  20. 10.1109/ICETEEEM.2012.6494498 · DOI
  21. dhiyanesh, 2016, F 2c: An Novel Distributed Denial Of Service Attack Mitigation Model for Saas Cloud Environment, Asian Journal of Research in Social Sciences & Humanities, 10.5958/2249-7315.2016.00389.0 · DOI
  22. 10.1109/ICACCCT.2012.6320763 · DOI

Cited by

Metadata

Authors
B, D., K, R. K., S, R., & R, R.
Venue
2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
Publisher
IEEE
Published
2021-11-11
Pages
1-8
DOI
10.1109/i-smac52330.2021.9640894
OpenAlex
W4200129393
Cited by
26
References
3
Citations by year
2025: 4 · 2024: 7 · 2023: 12 · 2022: 3
FWCI
1.80
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
object detection, video surveillance, convolutional neural networks, probabilistic neural network, video streaming

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