A Review on Human Activity Recognition System
DOI:
https://doi.org/10.26438/ijcse/v6i12.825829Keywords:
Human Activities, Segmentation, Feature Extraction,, Classification, Human Action RecognitionAbstract
Action recognition being one of the hot topics of many research efforts and being useful in so many commercial and scientific fields. Action recognition concerns the extraction ofactivity based knowledge, image data relationship or other patterns implicitly or explicitly stored in the images. Action in images is one of the powerful sources of high-level semantics. Recognition can be used for recognizing activities occurring in a particular scene. There is a need of effective and efficient methods to be encounter for recognizing the activities of human. The goal of this work is to study various recognition methods of common human actions represented in images. In this research, we present detailed insights on existing works and the methodologies used by researchers for recognizing the human activities. Comparison among different human activities by similarity systems is particularly challenging owing to the great variety of techniques implemented to represent likeness and the dependence that the results present of the used image dataset. This will be helpful to the researchers for their future research direction in this area.
References
[1] Chaitra B H, Anupama H S, Cauvery N K, “Human Action Recognition using Image Processing and Artificial Neural Networks”, International Journal of Computer Applications (0975 – 8887), Volume 80 No. 9, October 2013.
[2] Christian Thurau, Vaclav Hlavac, “Pose Primitive based Human Action Recognition in Videos or Still Images”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
[3] GhazaliSulong, Ammar Mohammedali, “Recognition of Human Activities from Still Image using Novel Classifier”, Journal of Theoretical and Applied Information Technology, Vol. 71 No. 1, 10th January 2015.
[4] GuodongGuo, Alice Lai, “A Survey on Still Image based Human Action Recognition”, Journal of Pattern Recognition, Vol. 47, pp- 3343 to 3361, 9th May 2014.
[5] Md. Atiqur Rahman Ahad, J.K. Tan, H.S. Kim and S. Ishikawa, “Human Activity Recognition: Various Paradigms”, International Conference on Control, Automation and Systems, pp-1896 to 1901, Oct. 14-17, 2008 in COEX, Seoul, Korea.
[6] MoinNabi, Mohammad Rahmati, “Human Action Recognition in Still Images using Bag of Latent Poselets”, CVMP2012 (Conference on Visual Media Production), 2012.
[7] Nabil Zerrrouki, FouziHarrou, Ying Sun, AmraneHouacine, “Adaboost-based Algorithm for Human Action Recognition”, 2017.
[8] NazliIkizler, R. GokberkCinbins, SelenPehlivan and Pinar Duygulu, “Recognizing Actions from Still Images”, Indian Council of Philosophical Research, ICPR, New Delhi,2007.
[9] Suganya V, “A Survey on Image Processing and Human Action Recognition”, International Journal of Latest Trends in Engineering and Technology (IJLTET), ISSN: 2278-621X, Vol. 6, Issue 1, 1st September 2015.
[10] Sumaira Ghazal, Umar S. Khan, “Human Posture Classification using Skeleton Information”, International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018.
[11] Weilong Yang, Yang Wang, and Greg Mori, “Recognizing Human Actions from Still Images with Latent Poses”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
[12] Zhichen Zhao, Huimin Ma, Shaodi You, “Single Image Action Recognition using Semantic Body Part Actions”, IEEE International Conference on Computer Vision (ICCV), 2017
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