ARCHIVES

1. S. Ranasinghe, F. Al Machot, and H. C. Mayr, “A review on applications of activity recognition systems with regard to performance and
evaluation,” Int. J. Distrib. Sens. Networks, vol. 12, no. 8, p. 1550147716665520, 2016.
2. T. Sztyler, H. Stuckenschmidt, and W. Petrich, “Position-aware activity recognition with wearable devices,” Pervasive Mob. Comput., vol.
38, pp. 281–295, 2017.
3. R. S. Monisa Nazir, Shalini Bhadola, Kirti Bhaia, “A Complete Analysis of Human Action Recognition Procedures,” Int. J. Trend Sci. Res.
Dev., vol. 6, no. 5, pp. 593–597, 2022.
4. R. S. Monisa Nazir, Kirti Bhatia, Shalini Bhadola, “Spatio-Temporal and Support Vector Machine Based Human Action Detection,” Int. J.
Multidiscip. Res. Sci. Eng. Technol. Manag., vol. 9, no. 7, pp. 1499–1505, 2022.
5. B. K. Chakraborty, D. Sarma, M. K. Bhuyan, and K. F. MacDorman, “Review of constraints on vision
‐based gesture recognition for human–
computer interaction,” IET Comput. Vis., vol. 12, no. 1, pp. 3–15, 2018.
6. D. Das Dawn and S. H. Shaikh, “A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector,”
Vis. Comput., vol. 32, pp. 289–306, 2016.
7. M. Meng, H. Drira, and J. Boonaert, “Distances evolution analysis for online and off-line human object interaction recognition,” Image Vis.
Comput., vol. 70, pp. 32–45, 2018.
8. B. Chakraborty, O. Rudovic, and J. Gonzalez, “View-invariant human-body detection with extension to human action recognition using
component-wise HMM of body parts,” in 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, 2008, pp. 1–
6.
9. M. N. Kumar and D. Madhavi, “Improved discriminative model for view-invariant human action recognition,” Int. J. Comput. Sci. Eng.
Technol, vol. 4, no. 3, pp. 1263–1270, 2013.
10. T. Syeda-Mahmood, A. Vasilescu, and S. Sethi, “Recognizing action events from multiple viewpoints,” in Proceedings IEEE Workshop on
Detection and Recognition of Events in Video, 2001, pp. 64–72.
11. A. Iosifidis, A. Tefas, and I. Pitas, “Neural representation and learning for multi-view human action recognition,” in The 2012 International
Joint Conference on Neural Networks (IJCNN), 2012, pp. 1–6.
12. R. Poppe, “Vision-based human motion analysis: An overview,” Comput. Vis. image Underst., vol. 108, no. 1–2, pp. 4–18, 2007.
13. E. Ramasso, C. Panagiotakis, M. Rombaut, D. Pellerin, and G. Tziritas, “Human shape-motion analysis in athletics videos for coarse to fine
action/activity recognition using transferable belief model,” Electron. Lett. Comput. Vis. Image Anal., vol. 7, no. 4, pp. 32–50, 2009.
14. J. W. Davis and A. F. Bobick, “The representation and recognition of human movement using temporal templates,” in Proceedings of IEEE
Computer Society Conference on Computer Vision and Pattern Recognition, 1997, pp. 928–934.
15. Y. Ke, R. Sukthankar, and M. Hebert, “Event detection in crowded videos,” in 2007 IEEE 11th international conference on computer vision,
2007, pp. 1–8.
16. B. Ni, G. Wang, and P. R. Moulin, “H., 2011. A colour-depth video database for human daily activity recognition,” in Proceedings of IEEE
International Conference on ComputerVision Workshops, ICCV Workshops, November, pp. 6–13.
17. W. Li, Z. Zhang, and Z. Liu, “Action recognition based on a bag of 3d points,” in 2010 IEEE computer society conference on computer vision
and pattern recognition-workshops, 2010, pp. 9–14.
18. C. Chen, R. Jafari, and N. Kehtarnavaz, “Action recognition from depth sequences using depth motion maps-based local binary patterns,”
in 2015 IEEE winter conference on applications of computer vision, 2015, pp. 1092–109919. N. E. D. El Madany, Y. He, and L. Guan, “Human action recognition using temporal hierarchical pyramid of depth motion map and keca,”
in 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), 2015, pp. 1–6.
20. A. W. Vieira, E. R. Nascimento, G. L. Oliveira, Z. Liu, and M. F. Campos, “Iberoamerican Congress on Pattern Recognition,” 2012.
21. O. Oreifej and Z. Liu, “Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences,” in Proceedings of the
IEEE conference on computer vision and pattern recognition, 2013, pp. 716–723.
22. S. Lacoste-Julien, F. Sha, and M. I. Jordan, “DiscLDA: discriminative learning for dimensionality reduction and classification. 21st Int Conf
on Neural Information Processing Systems,” 2008.
23. Efros, Berg, Mori, and Malik, “Recognizing action at a distance,” in Proceedings Ninth IEEE International Conference on Computer Vision,
2003, pp. 726–733.
24. Y. Ke, R. Sukthankar, and M. Hebert, “Efficient visual event detection using volumetric features,” in Tenth IEEE International Conference
on Computer Vision (ICCV’05) Volume 1, 2005, vol. 1, pp. 166–173.
25. L. Wang and H. Yu, “Springer Briefs in Molecular Science,” Springer Singapore, Singapore, vol. 10, pp. 978–981, 2018.
26. J. Wang, Z. Liu, Y. Wu, and J. Yuan, “Mining actionlet ensemble for action recognition with depth cameras,” in 2012 IEEE conference on
computer vision and pattern recognition, 2012, pp. 1290–1297.
27. R. Messing, C. Pal, and H. Kautz, “Activity recognition using the velocity histories of tracked keypoints,” in 2009 IEEE 12th international
conference on computer vision, 2009, pp. 104–111.
28. P. Bilinski and F. Bremond, “Contextual statistics of space-time ordered features for human action recognition,” in 2012 IEEE Ninth
International Conference on Advanced Video and Signal-Based Surveillance, 2012, pp. 228–233.
29. I. Laptev and T. Lindeberg, “Spatial Coherence for Visual Motion Analysis.” Springer, 2006.
30. S. Zaidenberg, P. Bilinski, and F. Brémond, “Towards unsupervised sudden group movement discovery for video surveillance,” in 2014
International Conference on Computer Vision Theory and Applications (VISAPP), 2014, vol. 2, pp. 388–395.
31. G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Workshop on statistical
learning in computer vision, ECCV, 2004, vol. 1, no. 1–22, pp. 1–2.
32. F. Perronnin and C. Dance, “Fisher kernels on visual vocabularies for image categorization,” in 2007 IEEE conference on computer vision
and pattern recognition, 2007, pp. 1–8