16.7.4.6.7 Convolutional Neural Networks for Human Action Recognition and Detection

Chapter Contents (Back)
Action Recognition. Human Actions. Learning. Neural Networks. Convolutional Neural Networks. CNN.
See also Human Action Detection, Human Action Recognition.

Ji, S.W.[Shui-Wang], Xu, W.[Wei], Yang, M.[Ming], Yu, K.[Kai],
3D Convolutional Neural Networks for Human Action Recognition,
PAMI(35), No. 1, January 2013, pp. 221-231.
IEEE DOI 1212
BibRef

Yang, M.[Ming], Lv, F.J.[Feng-Jun], Xu, W.[Wei], Yu, K.[Kai], Gong, Y.H.[Yi-Hong],
Human action detection by boosting efficient motion features,
ObjectEvent09(522-529).
IEEE DOI 0910
BibRef

Yang, M.[Ming], Lv, F.J.[Feng-Jun], Xu, W.[Wei], Gong, Y.H.[Yi-Hong],
Detection Driven Adaptive Multi-cue Integration for Multiple Human Tracking,
ICCV09(1554-1561).
IEEE DOI 0909
BibRef

Ijjina, E.P.[Earnest Paul], Chalavadi, K.M.[Krishna Mohan],
Human action recognition using genetic algorithms and convolutional neural networks,
PR(59), No. 1, 2016, pp. 199-212.
Elsevier DOI 1609
Convolutional neural network (CNN) BibRef
Earlier:
Human action recognition based on motion capture information using fuzzy convolution neural networks,
ICAPR15(1-6)
IEEE DOI 1511
BibRef
Earlier:
Human Action Recognition Using Action Bank Features and Convolutional Neural Networks,
DeepLearnV14(328-339).
Springer DOI 1504
convolution BibRef

Ijjina, E.P.[Earnest Paul], Chalavadi, K.M.[Krishna Mohan],
Human action recognition in RGB-D videos using motion sequence information and deep learning,
PR(72), No. 1, 2017, pp. 504-516.
Elsevier DOI 1708
Multi-modal, action, recognition BibRef

Liu, C.H.[Cai-Hua], Liu, J.[Jie], He, Z.C.[Zhi-Cheng], Zhai, Y.J.[Yu-Jia], Hu, Q.H.[Qing-Hua], Huang, Y.[Yalou],
Convolutional neural random fields for action recognition,
PR(59), No. 1, 2016, pp. 213-224.
Elsevier DOI 1609
Action recognition BibRef

Lei, J., Li, G.H.[Guo-Hui], Zhang, J., Guo, Q., Tu, D.[Dan],
Continuous action segmentation and recognition using hybrid convolutional neural network-hidden Markov model model,
IET-CV(10), No. 6, 2016, pp. 537-544.
DOI Link 1609
Gaussian processes BibRef

Yu, S.[Sheng], Cheng, Y.[Yun], Xie, L.[Li], Li, S.Z.[Shao-Zi],
Fully convolutional networks for action recognition,
IET-CV(11), No. 8, December 2017, pp. 744-749.
DOI Link 1712
BibRef

Shi, Y.M.[Ye-Min], Tian, Y.H.[Yong-Hong], Wang, Y.W.[Yao-Wei], Huang, T.J.[Tie-Jun],
Sequential Deep Trajectory Descriptor for Action Recognition With Three-Stream CNN,
MultMed(19), No. 7, July 2017, pp. 1510-1520.
IEEE DOI 1706
Cameras, Feature extraction, Histograms, Neural networks, Optical imaging, Streaming media, Trajectory, Action recognition, long-term motion, sequential deep trajectory descriptor (sDTD), three-stream, framework BibRef

Shi, Y.M.[Ye-Min], Tian, Y.H.[Yong-Hong], Wang, Y.W.[Yao-Wei], Zeng, W., Huang, T.J.[Tie-Jun],
Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network,
ICCV17(716-725)
IEEE DOI 1802
cognitive systems, image recognition, image representation, learning (artificial intelligence), neural nets, Visualization BibRef

Wang, P.[Peng], Cao, Y.Z.[Yuan-Zhouhan], Shen, C.H.[Chun-Hua], Liu, L.Q.[Ling-Qiao], Shen, H.T.[Heng Tao],
Temporal Pyramid Pooling-Based Convolutional Neural Network for Action Recognition,
CirSysVideo(27), No. 12, December 2017, pp. 2613-2622.
IEEE DOI 1712
Action recognition, convolutional neural network (CNN), temporal pyramid pooling BibRef

Wang, P.[Peng], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], Shen, H.T.[Heng Tao],
Order-aware convolutional pooling for video based action recognition,
PR(91), 2019, pp. 357-365.
Elsevier DOI 1904
Action recognition, Convolutional neural network, Temporal pooling BibRef

Wang, L.L.[Liang-Liang], Ge, L.Z.[Lian-Zheng], Li, R.F.[Rui-Feng], Fang, Y.J.[Ya-Jun],
Three-stream CNNs for action recognition,
PRL(92), No. 1, 2017, pp. 33-40.
Elsevier DOI 1705
Action, recognition BibRef

Wang, X.H.[Xuan-Han], Gao, L.L.[Lian-Li], Song, J.K.[Jing-Kuan], Shen, H.T.[Heng-Tao],
Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition,
SPLetters(24), No. 4, April 2017, pp. 510-514.
IEEE DOI 1704
Computer architecture BibRef

Wang, X., Gao, L., Wang, P., Sun, X., Liu, X.,
Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length,
MultMed(20), No. 3, March 2018, pp. 634-644.
IEEE DOI 1802
Computational modeling, Convolution, Feature extraction, Histograms, Action recognition BibRef

Zhang, B.W.[Bo-Wen], Wang, L.M.[Li-Min], Wang, Z.[Zhe], Qiao, Y.[Yu], Wang, H.L.[Han-Li],
Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs,
IP(27), No. 5, May 2018, pp. 2326-2339.
IEEE DOI 1804
BibRef
Earlier:
Real-Time Action Recognition with Enhanced Motion Vector CNNs,
CVPR16(2718-2726)
IEEE DOI 1612
convolution, feature extraction, feedforward neural nets, image motion analysis, image sequences, video signal processing, real-time processing BibRef

Jing, L.L.[Long-Long], Yang, X.D.[Xiao-Dong], Tian, Y.L.[Ying-Li],
Video you only look once: Overall temporal convolutions for action recognition,
JVCIR(52), 2018, pp. 58-65.
Elsevier DOI 1804
Video understanding, Video classification, Action recognition, Convolutional neural network BibRef

Zhao, S.C.[Shi-Chao], Liu, Y.B.[Yan-Bin], Han, Y.H.[Ya-Hong], Hong, R.C.[Ri-Chang], Hu, Q.H.[Qing-Hua], Tian, Q.[Qi],
Pooling the Convolutional Layers in Deep ConvNets for Video Action Recognition,
CirSysVideo(28), No. 8, August 2018, pp. 1839-1849.
IEEE DOI 1808
Feature extraction, Trajectory, Convolutional codes, Image recognition, Optical imaging, Encoding, Histograms, ConvNets, feature fusion BibRef

Yang, H.[Hao], Yuan, C.F.[Chun-Feng], Li, B.[Bing], Du, Y.[Yang], Xing, J.L.[Jun-Liang], Hu, W.M.[Wei-Ming], Maybank, S.J.[Stephen J.],
Asymmetric 3D Convolutional Neural Networks for action recognition,
PR(85), 2019, pp. 1-12.
Elsevier DOI 1810
Asymmetric 3D convolution, MicroNets, 3D-CNN, Action recognition BibRef

Zhuang, Z.D.[Zhen-Dong], Xue, Y.[Yang],
TS-ICNN: Time Sequence-Based Interval Convolutional Neural Networks for Human Action Detection and Recognition,
IEICE(E101-D), No. 10, October 2018, pp. 2534-2538.
WWW Link. 1810
BibRef

He, F.X.[Fei-Xiang], Liu, F.[Fayao], Yao, R.[Rui], Lin, G.S.[Guo-Sheng],
Local fusion networks with chained residual pooling for video action recognition,
IVC(81), 2019, pp. 34-41.
Elsevier DOI 1902
Action recognition, Residual connection, Local fusion, Deep convolutional network BibRef

Zalluhoglu, C.[Cemil], Ikizler-Cinbis, N.[Nazli],
Region based multi-stream convolutional neural networks for collective activity recognition,
JVCIR(60), 2019, pp. 170-179.
Elsevier DOI 1903
Collective activity recognition, Action recognition BibRef

Yang, K.[Ke], Shen, X.L.[Xiao-Long], Qiao, P.[Peng], Li, S.J.[Shi-Jie], Li, D.S.[Dong-Sheng], Dou, Y.[Yong],
Exploring frame segmentation networks for temporal action localization,
JVCIR(61), 2019, pp. 296-302.
Elsevier DOI 1906
Temporal CNN after 2D spatial CNN. Action detection, Temporal action localization, Convolutional Neural Network BibRef

Li, C., Zhang, B., Chen, C., Ye, Q., Han, J., Guo, G., Ji, R.,
Deep Manifold Structure Transfer for Action Recognition,
IP(28), No. 9, Sep. 2019, pp. 4646-4658.
IEEE DOI 1908
backpropagation, convolutional neural nets, feature extraction, image recognition, image representation, minimisation, ADMM-BP BibRef

Purwanto, D., Pramono, R.R.A., Chen, Y.T., Fang, W.H.,
Three-Stream Network With Bidirectional Self-Attention for Action Recognition in Extreme Low Resolution Videos,
SPLetters(26), No. 8, August 2019, pp. 1187-1191.
IEEE DOI 1908
BibRef
And: Corrections: SPLetters(27), 2020, pp. 2188-2188.
IEEE DOI 2012
feature extraction, image motion analysis, image recognition, image representation, image resolution, neural nets, deep learning BibRef

Purwanto, D., Pramono, R.R.A.[R. Renanda Adhi], Chen, Y., Fang, W.,
Extreme Low Resolution Action Recognition with Spatial-Temporal Multi-Head Self-Attention and Knowledge Distillation,
RLQ19(961-969)
IEEE DOI 2004
convolutional neural nets, feature extraction, image motion analysis, image recognition, image resolution, self attention BibRef

Li, Y.G.[Yong-Gang], Ge, R.[Rui], Ji, Y.[Yi], Gong, S.R.[Sheng-Rong], Liu, C.P.[Chun-Ping],
Trajectory-Pooled Spatial-Temporal Architecture of Deep Convolutional Neural Networks for Video Event Detection,
CirSysVideo(29), No. 9, September 2019, pp. 2683-2692.
IEEE DOI 1909
Trajectory, Event detection, Feature extraction, Computer vision, Computer architecture, Image motion analysis, Fuses, deep feature BibRef

Wang, L.M.[Li-Min], Xiong, Y.J.[Yuan-Jun], Wang, Z.[Zhe], Qiao, Y.[Yu], Lin, D.[Dahua], Tang, X.[Xiaoou], Van Gool, L.J.[Luc J.],
Temporal Segment Networks for Action Recognition in Videos,
PAMI(41), No. 11, November 2019, pp. 2740-2755.
IEEE DOI 1910
Videos, Training, Adaptation models, Analytical models, Visualization, Histograms, Action recognition, ConvNets BibRef

Wang, L.M.[Li-Min], Qiao, Y.[Yu], Tang, X.[Xiaoou], Van Gool, L.J.[Luc J.],
Actionness Estimation Using Hybrid Fully Convolutional Networks,
CVPR16(2708-2717)
IEEE DOI 1612
BibRef

Lee, D.G.[Dong-Gyu], Lee, S.W.[Seong-Whan],
Prediction of partially observed human activity based on pre-trained deep representation,
PR(85), 2019, pp. 198-206.
Elsevier DOI 1810
Pre-trained CNN, Human activity prediction, Human interaction, Sub-volume co-occurrence matrix BibRef

Soltanian, M., Ghaemmaghami, S.,
Hierarchical Concept Score Postprocessing and Concept-Wise Normalization in CNN-Based Video Event Recognition,
MultMed(21), No. 1, January 2019, pp. 157-172.
IEEE DOI 1901
Task analysis, Visualization, Event detection, Training, Feature extraction, Support vector machines, Semantics, mean average precision BibRef

Zhu, S.[Suguo], Fang, Z.Y.[Zhen-Ying], Wang, Y.[Yi], Yu, J.[Jun], Du, J.P.[Jun-Ping],
Multimodal activity recognition with local block CNN and attention-based spatial weighted CNN,
JVCIR(60), 2019, pp. 38-43.
Elsevier DOI 1903
Activity recognition, Multimodal, Visual attention BibRef

Chenarlogh, V.A.[Vahid Ashkani], Razzazi, F.[Farbod],
Multi-stream 3D CNN structure for human action recognition trained by limited data,
IET-CV(13), No. 3, April 2019, pp. 338-344.
DOI Link 1904
BibRef

Tu, Z., Xie, W., Dauwels, J., Li, B., Yuan, J.,
Semantic Cues Enhanced Multimodality Multistream CNN for Action Recognition,
CirSysVideo(29), No. 5, May 2019, pp. 1423-1437.
IEEE DOI 1905
Semantics, Feature extraction, Spatiotemporal phenomena, Visualization, Object segmentation, Object detection, multi-modalities BibRef

Kumar, N., Sukavanam, N.,
A weakly supervised CNN model for spatial localization of human activities in unconstraint environment,
SIViP(14), No. 5, July 2020, pp. 1009-1016.
Springer DOI 2006
BibRef

Li, J.[Jun], Liu, X.L.[Xiang-Long], Zhang, M.Y.[Ming-Yuan], Wang, D.Q.[De-Qing],
Spatio-temporal deformable 3D ConvNets with attention for action recognition,
PR(98), 2020, pp. 107037.
Elsevier DOI 1911
Action recognition, Spatio-temporal deformable, Attention mechanism, 3D ConvNets BibRef

Li, J.[Jun], Liu, X.L.[Xiang-Long], Zhang, W.X.[Wen-Xuan], Zhang, M.Y.[Ming-Yuan], Song, J.K.[Jing-Kuan], Sebe, N.[Nicu],
Spatio-Temporal Attention Networks for Action Recognition and Detection,
MultMed(22), No. 11, November 2020, pp. 2990-3001.
IEEE DOI 2010
Feature extraction, Task analysis, Computer architecture, Optical imaging, action detection BibRef

Agethen, S.[Sebastian], Hsu, W.H.[Winston H.],
Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos,
MultMed(22), No. 3, March 2020, pp. 819-829.
IEEE DOI 2003
Kernel, Videos, Task analysis, Convolution, Feature extraction, YouTube, Mathematical model, recurrent neural networks BibRef

Li, X.Q.[Xiao-Qiang], Xie, M.[Miao], Zhang, Y.[Yin], Ding, G.T.[Guang-Tai], Tong, W.Q.[Wei-Qin],
Dual attention convolutional network for action recognition,
IET-IPR(14), No. 6, 11 May 2020, pp. 1059-1065.
DOI Link 2005
BibRef

Atto, A.M.[Abdourrahmane Mahamane], Benoit, A.[Alexandre], Lambert, P.[Patrick],
Timed-image based deep learning for action recognition in video sequences,
PR(104), 2020, pp. 107353.
Elsevier DOI 2005
Data conditioning, Video analysis, Deep learning, Convolution frames, Hilbert space-filling curve, Violence detection BibRef

Yoon, D.H.[Da-Hye], Cho, N.G.[Nam-Gyu], Lee, S.W.[Seong-Whan],
A novel online action detection framework from untrimmed video streams,
PR(106), 2020, pp. 107396.
Elsevier DOI 2006
Online action detection, Untrimmed video stream, Future frame generation, 3D convolutional neural network, Long short-term memory BibRef

Mishra, S.R.[Soumya Ranjan], Mishra, T.K.[Tusar Kanti], Sanyal, G.[Goutam], Sarkar, A.[Anirban], Satapathy, S.C.[Suresh Chandra],
Real time human action recognition using triggered frame extraction and a typical CNN heuristic,
PRL(135), 2020, pp. 329-336.
Elsevier DOI 2006
Frame extraction, Human action recognition, CNN Frame extraction, CNN BibRef

Wu, H., Ma, X., Li, Y.,
Convolutional Networks With Channel and STIPs Attention Model for Action Recognition in Videos,
MultMed(22), No. 9, September 2020, pp. 2293-2306.
IEEE DOI 2008
Videos, Dynamics, Feature extraction, Image sequences, Convolution, deep networks BibRef

Xiao, J.[Jihai], Cui, X.H.[Xiao-Hong], Li, F.[Feng],
Human action recognition based on convolutional neural network and spatial pyramid representation,
JVCIR(71), 2020, pp. 102722.
Elsevier DOI 2009
Human action recognition, Spatial pyramid, Convolution neural networks, Cosine distance measure BibRef

Fu, B., Fu, S., Wang, L., Dong, Y., Ren, Y.,
Deep Residual Split Directed Graph Convolutional Neural Networks for Action Recognition,
MultMedMag(27), No. 4, October 2020, pp. 9-17.
IEEE DOI 2012
Neural networks, Convolutional neural networks, Training, Data mining, Feature extraction, Residual-split block BibRef


Lu, J., Nguyen, M., Yan, W.Q.,
Deep Learning Methods for Human Behavior Recognition,
IVCNZ20(1-6)
IEEE DOI 2012
Deep learning, Image recognition, Neural networks, Streaming media, Real-time systems, Spatiotemporal phenomena, Kernel, Attention mechanism BibRef

Yu, Z., Yan, W.Q.,
Human Action Recognition Using Deep Learning Methods,
IVCNZ20(1-6)
IEEE DOI 2012
Deep learning, Image recognition, Correlation, Noise measurement, Videos, Two-Stream CNN BibRef

Wu, J.C.[Jian-Chao], Kuang, Z.H.[Zhang-Hui], Wang, L.M.[Li-Min], Zhang, W.[Wayne], Wu, G.S.[Gang-Shan],
Context-aware RCNN: A Baseline for Action Detection in Videos,
ECCV20(XXV:440-456).
Springer DOI 2011
BibRef

Basly, H.[Hend], Ouarda, W.[Wael], Sayadi, F.E.[Fatma Ezahra], Ouni, B.[Bouraoui], Alimi, A.M.[Adel M.],
CNN-SVM Learning Approach Based Human Activity Recognition,
ICISP20(271-281).
Springer DOI 2009
BibRef

Liu, H.[Hong], Ren, B.[Bin], Liu, M.Y.[Meng-Yuan], Ding, R.W.[Run-Wei],
Grouped Temporal Enhancement Module for Human Action Recognition,
ICIP20(1801-1805)
IEEE DOI 2011
Videos, Kernel, Convolution, Solid modeling, Action Recognition, Video Classification BibRef

Hsieh, H.Y., Chen, D.J., Liu, T.L.,
Temporal Action Proposal Generation Via Deep Feature Enhancement,
ICIP20(1391-1395)
IEEE DOI 2011
Proposals, Feature extraction, Motion segmentation, Video sequences, Convolution, Task analysis, Detectors, Untrimmed Video BibRef

Sudhakaran, S., Escalera, S., Lanz, O.,
Gate-Shift Networks for Video Action Recognition,
CVPR20(1099-1108)
IEEE DOI 2008
GSM, Convolution, Feature extraction, Logic gates, Kernel BibRef

Ghosh, P., Yao, Y., Davis, L.S., Divakaran, A.,
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation,
WACV20(565-574)
IEEE DOI 2006
Neural networks, Videos, Image edge detection, Technological innovation, Strain, Task analysis. BibRef

Farha, Y.A.[Yazan Abu], Gall, J.[Jurgen],
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation,
CVPR19(3570-3579).
IEEE DOI 2002
BibRef

Wei, J., Wang, H., Yi, Y., Li, Q., Huang, D.,
P3D-CTN: Pseudo-3D Convolutional Tube Network for Spatio-Temporal Action Detection in Videos,
ICIP19(300-304)
IEEE DOI 1910
Human action detection, pseudo-3D con-volutional tube network, tube proposal BibRef

Martin, P., Benois-Pineau, J., Péteri, R., Morlier, J.,
Optimal Choice of Motion Estimation Methods for Fine-Grained Action Classification with 3D Convolutional Networks,
ICIP19(554-558)
IEEE DOI 1910
Deep Learning, Optical Flow, Data Normalization, Action classification, Spatio-temporal convolution BibRef

Wang, J., Du, Z., Li, A., Wang, Y.,
Atrous Temporal Convolutional Network for Video Action Segmentation,
ICIP19(1585-1589)
IEEE DOI 1910
Action segmentation, multiscale modeling, atrous temporal convolution, temporal pyramid pooling BibRef

Wu, C., Han, J., Li, X.,
Time-Asymmetric 3d Convolutional Neural Networks for Action Recognition,
ICIP19(21-25)
IEEE DOI 1910
Time-asymmetric 3D Convolution, 3D CNN, Action Recognition BibRef

Liu, Z.K.[Zhi-Kang], Wang, Z.L.[Zi-Lei], Zhao, Y.[Yan], Tian, Y.[Ye],
SMC: Single-Stage Multi-location Convolutional Network for Temporal Action Detection,
ACCV18(II:179-195).
Springer DOI 1906
BibRef

Das, S., Chaudhary, A., Bremond, F., Thonnat, M.,
Where to Focus on for Human Action Recognition?,
WACV19(71-80)
IEEE DOI 1904
convolutional neural nets, entropy, feature extraction, image classification, image motion analysis, object recognition, BibRef

Cong, G., Domeniconi, G., Shapiro, J., Yang, C., Chen, B.,
Video Action Recognition With an Additional End-to-End Trained Temporal Stream,
WACV19(51-60)
IEEE DOI 1904
convolutional neural nets, image motion analysis, image recognition, image sequences, object detection, Computer architecture BibRef

Cheng, C., Lv, P., Su, B.,
Spatiotemporal Pyramid Pooling in 3D Convolutional Neural Networks for Action Recognition,
ICIP18(3468-3472)
IEEE DOI 1809
Spatiotemporal phenomena, Training, Kinetic theory, Robustness, Computer architecture, Video Recognition BibRef

Bhattacharjee, P.[Prateep], Das, S.[Sukhendu],
Two-Stream Convolutional Network with Multi-level Feature Fusion for Categorization of Human Action from Videos,
PReMI17(549-556).
Springer DOI 1711
BibRef

Girdhar, R., Ramanan, D., Gupta, A., Sivic, J., Russell, B.,
ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification,
CVPR17(3165-3174)
IEEE DOI 1711
Aggregates, Computer architecture, Convolution, Feature extraction, Image recognition, Streaming media, Trajectory BibRef

Wang, Y., Long, M., Wang, J., Yu, P.S.,
Spatiotemporal Pyramid Network for Video Action Recognition,
CVPR17(2097-2106)
IEEE DOI 1711
Convolution, Optical imaging, Optical losses, Spatiotemporal phenomena, Training BibRef

Kar, A., Rai, N., Sikka, K., Sharma, G.,
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos,
CVPR17(5699-5708)
IEEE DOI 1711
Feature extraction, Hidden Markov models, Prediction algorithms, Standards, Training, Videos BibRef

Wang, Z.[Zhe], Wang, L.M.[Li-Min], Du, W.B.[Wen-Bin], Qiao, Y.[Yu],
Exploring Fisher vector and deep networks for action spotting,
ChaLearn15(10-14)
IEEE DOI 1510
Convolutional codes BibRef

Veeriah, V.[Vivek], Zhuang, N.F.[Nai-Fan], Qi, G.J.[Guo-Jun],
Differential Recurrent Neural Networks for Action Recognition,
ICCV15(4041-4049)
IEEE DOI 1602
LSTM: long short-term memory Neural Network. BibRef

Zhuang, N.F.[Nai-Fan], Yusufu, T.J.[Tuoerhong-Jiang], Ye, J.[Jun], Hua, K.A.[Kien A.],
Group Activity Recognition with Differential Recurrent Convolutional Neural Networks,
FG17(526-531)
IEEE DOI 1707
Activity recognition, Algorithm design and analysis, Computer architecture, Logic gates, Neural networks, Trajectory, Video, surveillance BibRef

Feichtenhofer, C.[Christoph], Pinz, A.[Axel], Zisserman, A.,
Convolutional Two-Stream Network Fusion for Video Action Recognition,
CVPR16(1933-1941)
IEEE DOI 1612
BibRef

Wang, Y.[Yifan], Song, J.[Jie], Wang, L.M.[Li-Min], Van Gool, L.J.[Luc J.], Hilliges, O.[Otmar],
Two-Stream SR-CNNs for Action Recognition in Videos,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Peng, X.J.[Xiao-Jiang], Schmid, C.[Cordelia],
Multi-region Two-Stream R-CNN for Action Detection,
ECCV16(IV: 744-759).
Springer DOI 1611
BibRef

Park, E., Han, X., Berg, T.L., Berg, A.C.,
Combining multiple sources of knowledge in deep CNNs for action recognition,
WACV16(1-8)
IEEE DOI 1606
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Zero-Shot, One-Shot, Few-Shot Learning for Human Action Recognition .


Last update:Apr 5, 2021 at 20:03:28