Open Access
Issue
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
Article Number 01147
Number of page(s) 9
DOI https://doi.org/10.1051/matecconf/202439201147
Published online 18 March 2024
  1. Sreenu, G., Saleem Durai, M.A. Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J Big Data 6, 48 (2019). https://doi.org/10.1186/s40537-019-0212-5 [CrossRef] [Google Scholar]
  2. V. C. Banu, I. M. Costea, F. C. Nemtanu and I. Bădescu, “Intelligent video surveillance system,” 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME), Constanta, Romania, 2017, pp. 208-212, doi: 10.1109/SIITME.2017.8259891. [Google Scholar]
  3. B. Cao, H. Xia and Z. Liu, “A Video Abnormal Behavior Recognition Algorithm Based on Deep Learning,” 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2021, pp. 755-759, doi: 10.1109/IMCEC51613.2021.9482114. [Google Scholar]
  4. George, J.F., Scheibe, K., Townsend, A.M. and Mennecke, B. (2018), “The amorphous nature of agile: no one size fits all”, Journal of Systems and Information Technology, Vol. 20 No. 2, pp. 241-260. https://doi.org/10.1108/JSIT-11-2017-0118 [CrossRef] [Google Scholar]
  5. Budati, A.K., Snv, G., Cherukupalli, K., P., A.K. and Moorthy T., V.K. (2021), “Highspeed data encryption technique with optimized memory based RSA algorithm for communications,” Circuit World, Vol. 47 No. 3, pp. 269-273. https://doi.org/10.1108/CW-10-2020-0282 [CrossRef] [Google Scholar]
  6. Y. Zhang, X. Zheng, W. Liang, S. Zhang and X. Yuan, “Visual Surveillance for Human Fall Detection in Healthcare IoT,” in IEEE MultiMedia, vol. 29, no. 1, pp. 36-46, 1 Jan.- March 2022, doi: 10.1109/MMUL.2022.3155768. [CrossRef] [Google Scholar]
  7. L. Zhao, X. Zhao, and S. Liu, “Advanced Motion Vector Difference Coding Beyond AV1,” 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 3631-3635, doi: 10.1109/ICIP46576.2022.9897971. [Google Scholar]
  8. S. O. N’guessan and N. Ling, “Human attention region-of interest in I-frame for video coding,” 2012 Visual Communications and Image Processing, San Diego, CA, USA, 2012, pp. 1-5, doi: 10.1109/VCIP.2012.6410793. [Google Scholar]
  9. Y. –K. Lai and L. –S. Lien, “Fast Motion Estimation Based on Diamond Refinement Search for High Efficiency Video Coding,” 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2019, pp. 1-2, doi: 10.1109/ICCE.2019.8661956. [Google Scholar]
  10. A. Doblander, A. Maier, B. Rinner, and H. Schwabach, “Improving fault-tolerance in intelligent video surveillance by monitoring, diagnosis, and dynamic reconfiguration,” Third International Workshop on Intelligent Solutions in Embedded Systems, 2005., Hamburg, Germany, 2005, pp. 194-201, doi: 10.1109/WISES.2005.1438728. [Google Scholar]
  11. L. Ding, Y. Tian, H. Fan, Y. Wang and T. Huang, “High-Efficiency Coding for Shaking Surveillance Videos Based on Global Motion Compensation,” 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), Taipei, Taiwan, 2016, pp. 259-265, doi: 10.1109/BigMM.2016.42. [Google Scholar]
  12. J. C. SanMiguel, J. M. Martínez and L. Caro-Campos, “Object-size invariant anomaly detection in video-surveillance,” 2017 International Carnahan Conference on Security Technology (ICCST), Madrid, Spain, 2017, pp. 1-6, doi: 10.1109/CCST.2017.8167826. [Google Scholar]
  13. V. Saligrama and Z. Chen, “Video anomaly detection based on local statistical aggregates,” 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 2112-2119, doi: 10.1109/CVPR.2012.6247917. [Google Scholar]
  14. Töreyin, B.U., Dedeoğlu, Y., Çetin, A.E. (2005). HMM-Based Falling Person Detection Using Both Audio and Video. In: Sebe, N., Lew, M., Huang, T.S. (eds) Computer Vision in Human-Computer Interaction. HCI 2005. Lecture Notes in Computer Science, vol 3766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573425_21 [Google Scholar]
  15. Zhu, L., Yongchareon, S. (2023). Application of Video Surveillance Intelligent Analysis System Based on KNN Algorithm. In: Abawajy, J.H., Xu, Z., Atiquzzaman, M., Zhang, X. (eds) Tenth International Conference on Applications and Techniques in Cyber Intelligence (ICATCI 2022). ICATCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 170. Springer, Cham. https://doi.org/10.1007/978-3-031-29097-8_72 [Google Scholar]
  16. Jiang, J., Zheng, Y., Shi, Z., et al. Towards privacy-preserving user targeting. J. Commun. Inf. Netw. 1, 22–32 (2016). https://doi.org/10.1007/BF03391577 [CrossRef] [Google Scholar]
  17. Pyla. Naresh, K. Ravindra and Dr. A. Chandra Sekhar, “The Secure Integrity Verification in Cloud Storage Auditing with Deduplication,” on IJCST vol.7, Issue 4,2016. [Google Scholar]
  18. P. G., S., R. K., N., Menon, V.G. et al. A secure data deduplication system for integrated cloud-edge networks. J Cloud Comp 9, 61 (2020). https://doi.org/10.1186/s13677-020-00214-6 [CrossRef] [Google Scholar]
  19. H. Sabirin and M. Kim, “Moving Object Detection and Tracking Using a Spatio-Temporal Graph in H.264/AVC Bitstreams for Video Surveillance,” in IEEE Transactions on Multimedia, vol. 14, no. 3, pp. 657-668, June 2012, doi: 10.1109/TMM.2012.2187777. [CrossRef] [Google Scholar]
  20. Bui, KH.N., Cho, J. & Yi, H. Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues. Appl Intell 52, 2763–2774 (2022). https://doi.org/10.1007/s10489-021-02587-w [CrossRef] [Google Scholar]
  21. G. Szwoch and P. Dalka, “Identification of regions of interest in video for a traffic monitoring system,” 2008 1st International Conference on Information Technology, Gdansk, Poland, 2008, pp. 1-4, doi: 10.1109/INFTECH.2008.4621654. [Google Scholar]
  22. Rodriguez M, Sivic J, Laptev I. Chapter 5—The analysis of high-density crowds in videos. In: Group and crowd behavior for computer vision. Cambridge: Academic Press. 2017. pp. 89–113. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.