Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Johansson G (1973) Visual perception of biological motion and a model for its analysis. Percept Psychophys 14(2):201–211
Bobick A, Davis J (2001) The recognition of human movement using temporal templates. IEEE Trans Pattern Anal Mach Intell 23(3):257–267
Yilmaz A, Shah M (2008) A differential geometric approach to representing the human actions. Comput Vis Image Underst 109(3):335–351
Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247–2253
Laptev I, Lindeberg T (2003) Space-time interest points. In: International conference on computer vision, pp 432–439
Niebles JC, Wang H, Fei-Fei L (2008) Unsupervised learning of human action categories using spatial-temporalwords. Int J Comput Vis 79(3):299–318
Wang L, Suter D (2007) Learning and matching of dynamic shape manifolds for human action recognition. IEEE Trans Image Process 16:1646–1661
Oliver N, Garg A, Horvits E (2004) Layered representations for learning and inferring office activity from multiple sensory channels. Comput Vis Image Underst 96:163–180
Li W, Zhang Z, Liu Z (2008) Expandable data-driven graphical modeling of human actions based on salient postures. IEEE Trans Circuits Syst Video Technol 18(11):1499–1510
Wang Y, Mori G (2011) Hidden part models for human action recognition: probabilistic versus max margin. IEEE Trans Pattern Anal Mach Intell 33(7):1310–1323
Wang P, Li W, Ogunbona P, Wan J, Escalera S (2018) RGB-D-based human motion recognition with deep learning: a survey. Comput Vis Image Underst 171:118–139
Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp 568–576
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489–4497
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231
Xingjian SHI, Chen Z, Wang H, Yeung D-Y, Wong W-K, Woo W (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp 802–810
Bilen H, Fernando B, Gavves E, Vedaldi A, Gould S (2016) Dynamic image networks for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3034–3042
Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI conference on artificial intelligence
Li C, Hou Y, Wang P, Li W (2017) Joint distance maps based action recognition with convolutional neural networks. IEEE Signal Process Lett 24: 624–628
Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal LSTM with trust gates for 3D human action recognition. In: Proceedings of the European conference on computer vision, pp 816–833
Li S, Li W, Cook C, Zhu C, Gao Y (2018) Independently recurrent neural network (INDRNN): building a longer and deeper RNN. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5457–5466
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer International Publishing
About this entry
Cite this entry
Li, W., Liu, Z., Zhang, Z. (2021). Activity Recognition. In: Ikeuchi, K. (eds) Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-63416-2_63
Download citation
DOI: https://doi.org/10.1007/978-3-030-63416-2_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63415-5
Online ISBN: 978-3-030-63416-2
eBook Packages: Computer ScienceReference Module Computer Science and Engineering