3D Convolutional Neural Networks for Human Action Recognition
作者： Shuiwang Ji , Kai Yu , Ming Yang , Wei Xu
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摘要: We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw …
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acm.org 参考文章(28)
Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric Xing, Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks european conference on computer vision. pp. 69- 82 ,(2008) , 10.1007/978-3-540-88690-7_6
T. Serre, L. Wolf, T. Poggio, Object recognition with features inspired by visual cortex computer vision and pattern recognition. ,vol. 2, pp. 994- 1000 ,(2005) , 10.1109/CVPR.2005.254
Jim Mutch, David G. Lowe, Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields computer vision and pattern recognition. ,vol. 80, pp. 45- 57 ,(2008) , 10.1007/S11263-007-0118-0
C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local SVM approach international conference on pattern recognition. ,vol. 3, pp. 32- 36 ,(2004) , 10.1109/ICPR.2004.747
Yoshua Bengio, Learning Deep Architectures for AI ,(2009)
H. Jhuang, T. Serre, L. Wolf, T. Poggio, A Biologically Inspired System for Action Recognition international conference on computer vision. pp. 1- 8 ,(2007) , 10.1109/ICCV.2007.4408988
Geoffrey E Hinton, Ruslan R Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks Science. ,vol. 313, pp. 504- 507 ,(2006) , 10.1126/SCIENCE.1127647
Kunihiko Fukushima, Neocognitron: A Self Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position Biological Cybernetics. ,vol. 36, pp. 193- 202 ,(1980) , 10.1007/BF00344251
Kai Yu, Yihong Gong, Wei Xu, Deep Learning with Kernel Regularization for Visual Recognition neural information processing systems. ,vol. 21, pp. 1889- 1896 ,(2008)
Andrew Y. Ng, Honglak Lee, Peter Pham, Yan Largman, Unsupervised feature learning for audio classification using convolutional deep belief networks neural information processing systems. ,vol. 22, pp. 1096- 1104 ,(2009)