Cite

[1] L. Akoglu, H. Tong and D. Koutra, Data MKnowl Disc, vol. 29, 626-688, 2015, https://doi.org/10.1007/s10618-014-0365-y. Search in Google Scholar

[2] C. Schüldt, I. Laptev and B. Caputo, “Recognizing human actions: a local SVM approach”, Proceedings of the 17th International Conference on Pattern Recognition, vol. 3, 32-36, 2004, doi: 10.1109/ICPR.2004.1334462. Open DOISearch in Google Scholar

[3] L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, “Actions as Space-Time Shapes”, Transactions on Pattern Analysis and Machine Intelligence, www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html. Search in Google Scholar

[4] M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, “Actions as Space-Time Shapes”, 10th IEEE International Conference on Computer Vision, 1395-1402, 2005, www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html.10.1109/ICCV.2005.28 Search in Google Scholar

[5] A. Abdulmunem, L. Y-Kun, A. Hassan, and S. Xianfang, “Human action recognition using graph matching”, 7th International Conference on Applied Science and Technology, Proc. 2144. 050003-1-050003-10, 2019, https://doi.org/10.1063/1.5123119. Search in Google Scholar

[6] B. Yang, C. Jinmeng, N. Rongrong, and L. Zou, “Anomaly Detection in Noving Crowds through Spatiotemporal Autoencoding and Additional Attention”, Advances Multimedia, https://doi/org/10.1155/2018/2087574. Search in Google Scholar

[7] H. Vu, T. D. Nguyen, T. Le, W. Luo, and D. Phung, “Robust anomaly detection in videos using multilevel representations”, Proceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence, 5216-5223, 2019, https://doi.org/10.1609/aaai.v33i01.33015216. Search in Google Scholar

[8] H. Jingtao, E. Zhu, S. Wang, X. Liu, X. Guo and J. Jin, “An E cient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos”, Sensors, 4145, 2019, https://doi.org/10.3390/s19194145.680624331554333 Search in Google Scholar

[9] R. Kapoor O. Mishra and M. Tripathi, “Anomaly detection in group activities based on fuzzy lattices using Schrdinger equation”, Iran Journal of Computer Science, https://doi.org/10.1007/s42044-019-00045-y. Search in Google Scholar

[10] L. Shi, Y. Zhang, J. Cheng, and H. Lu “Skeleton-Based Action Recognition With Directed Graph Neural Networks”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, doi: 10.1109/CVPR.2019.00810. Open DOISearch in Google Scholar

[11] W. Hao, R. Zhang, S. Li, J. Li, F. Zhao, and Z. Zhang, “Anomaly Event Detection in Security Surveillance Using Two-Stream Based Model”, Security and Communication Networks, 2020, Article ID 8876056, 2020, https://doi.org/10.1155/2020/8876056. Search in Google Scholar

[12] D. Singh and K. Mohan, “Graph formulation of video activities for abnormal activity recognition”, Pattern Recognition, 65, 265-272, 2017, https://doi.org/10.1016/j.patcog.2017.01.001. Search in Google Scholar

[13] W. Eberle and L. Holder “Discovering Structural Anomalies in Graph-Based Data”, Proceedings - IEEE International Conference on Data Mining, 393-398, 2007, 10.1109/ICDMW.2007.91.10.1109/ICDMW.2007.91 Search in Google Scholar

[14] T. Pourhabibi, K. L. Ong, B. H. Kam, and Y. L. Boo, “Fraud detection: A systematic literature review of graph-based anomaly detection approaches”, Decision Support Systems, vol. 133,2020, https://doi.org/10.1016/j.dss.2020.113303. Search in Google Scholar

[15] L. Davis, W. Liu, P. Miller, and G. Redpath, “Detecting anomalies in graphs with numeric labels”, Proceedings of the 20th ACM international conference on Information and knowledge management, Association for Computing Machinery, New York, NY, USA, 1197–1202, 2011, doi:https://doi.org/10.1145/2063576.2063749. Search in Google Scholar

[16] C. Noble and D. Cook, “Graph-based anomaly detection”, Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, https://doi.org/10.1145/956750.956831. Search in Google Scholar

[17] D. Cook and L. Holder, “Graph-Based Data Mining”, IEEE Intelligent Systems. Search in Google Scholar

[18] I. Laptev, “On Space-Time Interest Points”, Int J Comput Vision, 107–123, 2005, https://doi.org/10.1007/s11263-005-1838-7. Search in Google Scholar

[19] I. Laptev and T. Lindberg, “Space-time interest points”, International Journal of Computer Vision - IJCV, vol. 1, (2003), https://doi.org/10.1109/ICCV.2003.1238378. Search in Google Scholar

[20] R. Kapoor, O Mishra, and M. Tripathi, “Human action recognition using descriptor based on selective finite element analysis”, Journal of Electrical Engineering, 443-453, 2019, https://doi.org/10.2478/jee-2019-0077. Search in Google Scholar

[21] O. Mishra, R. Kapoor, and M.Tripathi, “Human Action Recognition Using Modified Bag of Visual Word based on Spectral Perception”, International Journal of Image, Graphics and Signal Processing, 11, 34-43, 2019, https://doi.org/10.5815/ijigsp.2019.09.04. Search in Google Scholar

[22] I. Bellamine and H. Tairi, “Motion Detection using the space-time interest points”, Journal of Computer Science, 828-839, 2014, https://doi.org/10.3822/jcssp.2014.828.839. Search in Google Scholar

[23] P. Dollar, V. Rabaut, G. Cottrell, and S. Belongie, “Behavior recognition via sparse spatio-temporal features”, Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 15-16, IEEE Xplore Press, 65-72, 2005, doi: 10.1109/VSPETS.2005.1570899. Open DOISearch in Google Scholar

[24] G. Willems, T. Tuytelaars, and L. V. Gool, “An efficient dense and scale-invariant spatio-temporal interest point detector”, Proceedings 10th European Conference on Computer Vision, 12-18, Springer Berlin Heidelberg, Marseille, France. 650-663 (2008). doi: 10.1007/978-3-540-88688-4 48. Open DOISearch in Google Scholar

[25] Q. Xiao, J. Liu, Q. Wang, Z. Jiang, X. Wang, and Y. Yao, “Towards Network Anomaly Detection Using Graph Embedding”, In: Krzhizhanovskaya V.V. et al (eds) Computational Science, vol. 12140, Springer, 2020, https://doi.org/10.1007/978-3-030-5023-612. Search in Google Scholar

eISSN:
1339-309X
Language:
English
Publication timeframe:
6 times per year
Journal Subjects:
Engineering, Introductions and Overviews, other