Abstract
Human action recognition is a challenging area of research in the field of computer vision due to its complex analytical structure. Researches have been carried out in this field over the past few years. Now, as it has become possible to recognize human action successfully, the researchers are trying to make the recognition system real time in nature. But due to its complex video analysis, it has not been possible for the researchers to make it real time completely. Though some algorithms had been developed that will successfully recognize human action with the minimum delay possible in order to make the recognition system almost real time in nature. In this work, we have compared some of the successful human action recognition methodologies along with the recent trends to accomplish the task in negligible time delay.
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References
Antic B, Milbich T, Ommer B (2013) Less is more: video trimming for action recognition. In: 2013 IEEE international conference on computer vision workshops, Dec 2013, pp 515–521
Dhamsania CJ, Ratanpara TV (2016) A survey on human action recognition from videos. In: 2016 online international conference on green engineering and technologies (IC-GET), Nov 2016, pp 1–5
Kale GV, Patil VH (2016) A study of vision based human motion recognition and analysis. CoRR, abs/1608.06761
Kong Y, Jia Y, Fu Y (2014) Interactive phrases: semantic descriptions for human interaction recognition. IEEE Trans Pattern Anal Mach Intell 36(9):1775–1788
Li Y, Lu Q, Luo W (2016) Adaptive temporal sampling for real-time human action recognition. In: 2016 IEEE advanced information management, communicates, electronic and automation control conference (IMCEC), Oct 2016, pp 1306–1310
MarÃn-Jiménez MJ, de la Blanca NP (2013) Human interaction recognition by motion decoupling. In: Sanches JM, Micó L, Cardoso JS (eds) Pattern recognition and image analysis. Springer, Berlin, Heidelberg, pp 374–381
Nguyen N, Yoshitaka A (2014) Human interaction recognition using independent subspace analysis algorithm. In: 2014 IEEE international symposium on multimedia, Dec 2014, pp 40–46
Talha SAW, Hammouche M, Ghorbel E, Fleury A, Ambellouis S (2018) Features and classification schemes for view-invariant and real-time human action recognition. IEEE Trans Cogn Dev Syst 10(4):894–902
Wang H, Klser A, Schmid C, Liu C (2011) Action recognition by dense trajectories. CVPR 2011:3169–3176
Xiao Q, Si Y (2017) Human action recognition using autoencoder. In: 2017 3rd IEEE international conference on computer and communications (ICCC), Dec 2017, pp 1672–1675
Yun K, Honorio J, Chattopadhyay D, Berg TL, Samaras D (2012) Two-person interaction detection using body-pose features and multiple instance learning. In: 2012 IEEE Computer Society conference on computer vision and pattern recognition workshops, June 2012, pp 28–35
Zhang B, Yan Y, Conci N, Sebe N (2014) You talkin’ to me?: recognizing complex human interactions in unconstrained videos. In: Proceedings of the 22Nd ACM international conference on multimedia, MM ’14, New York, NY, USA, 2014. ACM, pp 821–824
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Das, B., Saha, A. (2021). A Survey on Current Trends in Human Action Recognition. In: Mukherjee, M., Mandal, J., Bhattacharyya, S., Huck, C., Biswas, S. (eds) Advances in Medical Physics and Healthcare Engineering. Lecture Notes in Bioengineering. Springer, Singapore. https://doi.org/10.1007/978-981-33-6915-3_44
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DOI: https://doi.org/10.1007/978-981-33-6915-3_44
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