Action recognition with trajectory-pooled deep-convolutional descriptors
Open accessAbstract
Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features [31] and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMD-B51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features [31] and deep-learned features [24]. Our method also achieves superior performance to the state of the art on these datasets.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Performance of ED-TCN is on par with the ensemble approach of Singh et al. [28] on GTEA.
“Performance of the ED-TCN is on par with the ensemble approach of Singh et al . [28], which combines EgoNet features with TDD [17].”
From Temporal Convolutional Networks for Action Segmentation and Detection (2017) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · see citing works
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · see citing works
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,222 · see citing works
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 4,749 · see citing works
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., & Shah, M. (2012) · arXiv · doi:10.48550/arxiv.1212.0402 · Cited by 4,575 · see citing works
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965 · see citing works
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481 · see citing works
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407 · see citing works
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335 · see citing works
Ivan Laptev, A. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-1838-7 · Cited by 2,631 · see citing works
Behavior Recognition via Sparse Spatio-Temporal Features
Dollár, P., Rabaud, V., Cottrell, G. W., & Belongie, S. (2006) · IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) · doi:10.1109/vspets.2005.1570899 · Cited by 2,469 · see citing works
Fischler, M. A., & Bolles, R. C. (1987) · Elsevier eBooks · doi:10.1016/b978-0-08-051581-6.50070-2 · Cited by 2,192 · see citing works
Aggarwal, J. K., & Ryoo, M. S. (2011) · ACM Computing Surveys · doi:10.1145/1922649.1922653 · Cited by 2,053 · see citing works
A Spatio-Temporal Descriptor Based on 3D-Gradients
Klaeser, A., Marszałek, M., & Schmid, C. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.99 · Cited by 1,842 · see citing works
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669 · see citing works
A Duality Based Approach for Realtime TV-L 1 Optical Flow
Zach, C., Pock, T., & Bischof, H. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74936-3_22 · Cited by 1,658 · cited 1× · see citing works
“We choose the TVL1 optical flow algorithm [40] and use the OpenCV implementation, due to its balance between accuracy and efficiency.”
MethodImage Classification with the Fisher Vector: Theory and Practice
Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498 · see citing works
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461 · see citing works
Laptev, & Tony Lindeberg, C. (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238378 · Cited by 1,301 · see citing works
Evaluation of local spatio-temporal features for action recognition
Wang, H., Ullah, M. M., Kläser, A., Laptev, I., & Schmid, C. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.124 · Cited by 1,295 · see citing works
Large-scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G. D., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · Cited by 1,266 · see citing works
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
Willems, G., Tuytelaars, T., & Gool, L. V. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_48 · Cited by 877 · see citing works
Action bank: A high-level representation of activity in video
Sadanand, S., & Corso, J. J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247806 · Cited by 696 · see citing works
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice
Peng, X., Wang, L., Wang, X., & Qiao, Y. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.03.013 · Cited by 681 · see citing works
Self-citationConvolutional Learning of Spatio-temporal Features
Taylor, G. W., Fergus, R., LeCun, Y., & Bregler, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_11 · Cited by 645 · see citing works
HMDB51: A Large Video Database for Human Motion Recognition
Kuehne, H., Jhuang, H., Stiefelhagen, R., & Serre, T. (2012) · Springer eBooks · doi:10.1007/978-3-642-33374-3_41 · Cited by 377 · see citing works
Gradient-based learning applied to document recognition
LeCun, Y. (2024) · doi:10.57702/yobif9bg · Cited by 323 · see citing works
Multi-view Super Vector for Action Recognition
Cai, Z., Wang, L., Peng, X., & Qiao, Y. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.83 · Cited by 205 · see citing works
Self-citationMotionlets: Mid-level 3D Parts for Human Motion Recognition
Wang, L., Qiao, Y., & Tang, X. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.345 · Cited by 179 · see citing works
Self-citationStructured Learning of Human Interactions in TV Shows
Patron-Perez, A., Marszałek, M., Reid, I. D., & Zisserman, A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.24 · Cited by 171 · see citing works
Video Action Detection with Relational Dynamic-Poselets
Wang, L., Qiao, Y., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_37 · Cited by 124 · see citing works
Self-citationA Comparative Study of Encoding, Pooling and Normalization Methods for Action Recognition
Wang, X., Wang, L., & Qiao, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37431-9_44 · Cited by 119 · see citing works
Self-citationLatent Hierarchical Model of Temporal Structure for Complex Activity Classification
Wang, L., Qiao, Y., & Tang, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2295753 · Cited by 102 · see citing works
Self-citationAction Recognition with Actons
Zhu, J., Wang, B., Yang, X., Zhang, W., & Tu, Z. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.442 · Cited by 88 · see citing works
Mining Motion Atoms and Phrases for Complex Action Recognition
Wang, L., Qiao, Y., & Tang, X. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.333 · Cited by 77 · see citing works
Self-citationLarge-scale web video event classification by use of Fisher Vectors
Sun, C., & Nevatia, R. (2013) · Proceedings · doi:10.1109/wacv.2013.6474994 · Cited by 75 · see citing works
Flagged first, then by citations
2 of the 49 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [32][32] H. Wang and C. Schmid. LEAR-INRIA submission for the thumos workshop. In ICCV Workshop on THUMOS Challenge, 2013. 2, 7, 8
Cited by
Sorted by citations
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · see citing works
“In addition to endto-end modeling on raw video inputs, it has been found that optical flow [45] and trajectories [50, 51] can be helpful.”
BackgroundQuo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570 · see citing works
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410 · see citing works
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Gool, L. V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_2 · Cited by 4,164 · see citing works
Self-citationA Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077 · see citing works
Self-citationConvolutional Two-Stream Network Fusion for Video Action Recognition
Feichtenhofer, C., Pinz, A. J., & Zisserman, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.213 · Cited by 2,842 · see citing works
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Hara, K., Kataoka, H., & Satoh, Y. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00685 · Cited by 2,300 · see citing works
Temporal Convolutional Networks for Action Segmentation and Detection
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.113 · Cited by 2,238 · see citing works
“[29], which combines their EgoNet features with TDD [18].”
BackgroundSelf-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036 · see citing works
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
Qiu, Z., Yao, T., & Mei, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.590 · Cited by 1,944 · see citing works
Learning Temporal Regularity in Video Sequences
Hasan, M., Choi, J., Neumann, J., Roy–Chowdhury, A. K., & Davis, L. S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.86 · Cited by 1,510 · see citing works
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
Lea, C., Vidal, R., Reiter, A., & Hager, G. D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_7 · Cited by 1,059 · see citing works
Long-Term Temporal Convolutions for Action Recognition
Varol, G., Laptev, I., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2712608 · Cited by 979 · see citing works
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works
Temporal Segment Networks for Action Recognition in Videos
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Gool, L. V. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2868668 · Cited by 928 · see citing works
Self-citationTemporal Action Detection with Structured Segment Networks
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., & Lin, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.317 · Cited by 892 · see citing works
Self-citationHuman Action Recognition and Prediction: A Survey
Kong, Y., & Fu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01594-9 · Cited by 832 · see citing works
Videos as Space-Time Region Graphs
Wang, X., & Gupta, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_25 · Cited by 786 · see citing works
Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition
Hara, K., Kataoka, H., & Satoh, Y. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.373 · Cited by 726 · see citing works
Delving Deeper into Convolutional Networks for Learning Video RepresentationsPreprint
Ballas, N., Yao, L., Pal, C., & Courville, A. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06432 · Cited by 715 · see citing works
Human Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 691 · see citing works
Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice
Peng, X., Wang, L., Wang, X., & Qiao, Y. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.03.013 · Cited by 681 · see citing works
Self-citationGoing deeper into action recognition: A survey
Herath, S., Harandi, M. T., & Porikli, F. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.010 · Cited by 646 · see citing works
Spatiotemporal Residual Networks for Video Action RecognitionPreprint
Feichtenhofer, C., Pinz, A. J., & Wildes, R. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02155 · Cited by 643 · see citing works
Spatio-Temporal AutoEncoder for Video Anomaly Detection
Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., & Hua, X. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123451 · Cited by 593 · see citing works
UntrimmedNets for Weakly Supervised Action Recognition and Detection
Wang, L., Xiong, Y., Lin, D., & Gool, L. V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.678 · Cited by 554 · see citing works
Self-citationTDN: Temporal Difference Networks for Efficient Action Recognition
Wang, L., Tong, Z., Ji, B., & Wu, G. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00193 · Cited by 513 · see citing works
Self-citationA Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503 · see citing works
“Many deep learning-based action representation methods have been proposed in the field of human action recognition [19,20,42,45,46,82].”
BackgroundTURN TAP: Temporal Unit Regression Network for Temporal Action Proposals
Gao, J., Yang, Z., Sun, C., Chen, K., & Nevatia, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.392 · Cited by 498 · see citing works
ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification
Girdhar, R., Ramanan, D., Gupta, A., Šivic, J., & Russell, B. C. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.337 · Cited by 494 · see citing works
VideoLSTM convolves, attends and flows for action recognition
Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., & Snoek, C. G. M. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.10.011 · Cited by 485 · see citing works
Temporal Pyramid Network for Action Recognition
Yang, C., Xu, Y., Shi, J., Dai, B., & Zhou, B. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00067 · Cited by 475 · see citing works
Pareek, P., & Thakkar, A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09904-8 · Cited by 466 · see citing works
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
Singh, B., Marks, T. K., Jones, M., Tuzel, O., & Shao, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.216 · Cited by 461 · see citing works
Real-Time Action Recognition with Enhanced Motion Vector CNNs
Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.297 · Cited by 444 · see citing works
Self-citationA review of Convolutional-Neural-Network-based action recognition
Yao, G., Lei, T., & Zhong, J. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.05.018 · Cited by 437 · see citing works
Temporal Generative Adversarial Nets with Singular Value Clipping
Saito, M., Matsumoto, E., & Saito, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.308 · Cited by 412 · see citing works
Appearance-and-Relation Networks for Video Classification
Wang, L., Li, W., Li, W., & Gool, L. V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00155 · Cited by 407 · see citing works
Self-citationRGB-D-based human motion recognition with deep learning: A survey
Wang, P., Li, W., Ogunbona, P., Wan, J., & Escalera, S. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.04.007 · Cited by 405 · see citing works
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks
Wang, P., Li, Z., Hou, Y., & Li, W. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967191 · Cited by 391 · see citing works
Convolutional Two-Stream Network Fusion for Video Action RecognitionPreprint
Feichtenhofer, C., Pinz, A. J., & Zisserman, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06573 · Cited by 384 · see citing works
Towards Good Practices for Very Deep Two-Stream ConvNetsPreprint
Wang, L., Xiong, Y., Wang, Z., & Qiao, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.02159 · Cited by 381 · see citing works
Self-citationMulti-region Two-Stream R-CNN for Action Detection
Peng, X., & Schmid, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_45 · Cited by 369 · see citing works
Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368 · see citing works
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
Sun, S., Kuang, Z., Sheng, L., Ouyang, W., & Zhang, W. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00151 · Cited by 347 · see citing works
Yadav, S. K., Tiwari, K., Pandey, H. M., & Akbar, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106970 · Cited by 328 · see citing works
Learnable pooling with Context Gating for video classificationPreprint
Miech, A., Laptev, I., & Šivic, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06905 · Cited by 295 · see citing works
Going Deeper into First-Person Activity Recognition
Ma, M., Fan, H., & Kitani, K. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.209 · Cited by 281 · see citing works
A review of state-of-the-art techniques for abnormal human activity recognition
Dhiman, C., & Vishwakarma, D. K. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.08.014 · Cited by 275 · see citing works
Spatiotemporal Pyramid Network for Video Action Recognition
Wang, Y., Long, M., Wang, J., & Yu, P. S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.226 · Cited by 271 · see citing works
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
Shahroudy, A., Ng, T.-T., Gong, Y., & Wang, G. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2691321 · Cited by 266 · see citing works
Action Recognition with Dynamic Image Networks
Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2769085 · Cited by 262 · see citing works
Bagautdinov, T., Alahi, A., Fleuret, F., Fua, P., & Savarese, S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.365 · Cited by 261 · see citing works
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
Giancola, S., Amine, M., Dghaily, T., & Ghanem, B. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00223 · Cited by 261 · see citing works
Multi-stream CNN: Learning representations based on human-related regions for action recognition
Tu, Z., Xie, W. C., Qin, Q., Poppe, R. W., Veltkamp, R. C., Li, B., & Yuan, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.01.020 · Cited by 250 · see citing works
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video ClassificationPreprint
Diba, A., Fayyaz, M., Sharma, V., Karami, A. H., Arzani, M. M., Yousefzadeh, R., & Gool, L. V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08200 · Cited by 250 · see citing works
MiCT: Mixed 3D/2D Convolutional Tube for Human Action Recognition
Zhou, Y., Sun, X., Zha, Z.-J., & Zeng, W. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00054 · Cited by 247 · see citing works
TS-LSTM and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition
Ma, C., Chen, M.-H., Kira, Z., & AlRegib, G. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.09.003 · Cited by 247 · see citing works
A Key Volume Mining Deep Framework for Action Recognition
Zhu, W., Hu, J., Sun, G., Cao, X., & Qiao, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.219 · Cited by 245 · see citing works
Self-citationSleep Quality Prediction From Wearable Data Using Deep Learning
Sathyanarayana, A., Joty, S., Fernández-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A. K., Arora, T., & Taheri, S. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6562 · Cited by 242 · see citing works
Zolfaghari, M., Oliveira, G. L., Sedaghat, N., & Brox, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.316 · Cited by 242 · see citing works
Deep Temporal Linear Encoding Networks
Diba, A., Sharma, V., & Gool, L. V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.168 · Cited by 236 · see citing works
YOLO based Human Action Recognition and Localization
Shinde, S., Kothari, A., & Gupta, V. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.112 · Cited by 236 · see citing works
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Long, X., Gan, C., de Melo, G., Wu, J., Liu, X., & Wen, S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00817 · Cited by 233 · see citing works
A Survey of Content-Aware Video Analysis for Sports
Shih, H.-C. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2655624 · Cited by 230 · see citing works
End-to-End Learning of Motion Representation for Video Understanding
Fan, L., Huang, W., Gan, C., Ermon, S., Gong, B., & Huang, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00630 · Cited by 230 · see citing works
Action recognition based on joint trajectory maps with convolutional neural networks
Wang, P., Li, W., Li, C., & Hou, Y. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.029 · Cited by 230 · see citing works
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., Prabhu, N., Kruthiventi, S. S. S., & Babu, R. V. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00036 · Cited by 229 · see citing works
Islam, M. M., Nooruddin, S., Karray, F., & Muhammad, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106060 · Cited by 229 · see citing works
Wang, X., Farhadi, A., & Gupta, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.291 · Cited by 228 · see citing works
Sequential Deep Trajectory Descriptor for Action Recognition With Three-Stream CNN
Shi, Y., Tian, Y., Wang, Y., & Huang, T. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2666540 · Cited by 221 · see citing works
Recurrent Spatial-Temporal Attention Network for Action Recognition in Videos
Du, W., Wang, Y., & Qiao, Y. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2778563 · Cited by 218 · see citing works
Self-citationAsymmetric 3D Convolutional Neural Networks for action recognition
Yang, H., Yuan, C., Li, B., Du, Y., Xing, J., Hu, W., & Maybank, S. J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.028 · Cited by 210 · see citing works
Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes
Yang, J., Zou, H., Jiang, H., & Xie, L. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2849655 · Cited by 208 · see citing works
Lattice Long Short-Term Memory for Human Action Recognition
Sun, L., Jia, K., Chen, K., Yeung, D., Shi, B. E., & Savarese, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.236 · Cited by 206 · see citing works
Rethinking Spatiotemporal Feature Learning For Video Understanding.
Xie, S., Sun, C., Huang, J., Tu, Z., & Murphy, K. P. (2017) · Cited by 205 · see citing works
Understanding cities with machine eyes: A review of deep computer vision in urban analytics
Ibrahim, M. R., Haworth, J., & Cheng, T. (2019) · Cities · doi:10.1016/j.cities.2019.102481 · Cited by 204 · see citing works
Temporal Deformable Residual Networks for Action Segmentation in Videos
Lei, P., & Todorovic, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00705 · Cited by 202 · see citing works
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos
Du, W., Wang, Y., & Qiao, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.402 · Cited by 199 · see citing works
Self-citationSpatio-temporal Channel Correlation Networks for Action Classification
Diba, A., Fayyaz, M., Sharma, V., Arzani, M. M., Yousefzadeh, R., Gall, J., & Gool, L. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_18 · Cited by 199 · see citing works
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
Gao, X., Hu, W., Tang, J., Liu, J., & Guo, Z. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3351170 · Cited by 194 · see citing works
Georgiou, T., Liu, Y., Chen, W., & Lew, M. S. (2019) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-019-00183-w · Cited by 194 · see citing works
First Person Action Recognition Using Deep Learned Descriptors
Singh, S., Arora, C., & Jawahar, C. V. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.287 · Cited by 192 · see citing works
Unsupervised Learning of Long-Term Motion Dynamics for Videos
Luo, Z., Peng, B., Huang, D.-A., Alahi, A., & Fei-Fei, L. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.751 · Cited by 189 · see citing works
Vision-based human action recognition: An overview and real world challenges
Jegham, I., Khalifa, A. B., Alouani, I., & Mahjoub, M. A. (2020) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2019.200901 · Cited by 187 · see citing works
Sargano, A. B., Angelov, P. P., & Habib, Z. (2017) · Applied Sciences · doi:10.3390/app7010110 · Cited by 185 · see citing works
Vision-based human fall detection systems using deep learning: A review
Alam, E., Sufian, A., Dutta, P., & Leo, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105626 · Cited by 182 · see citing works
Spatio-Temporal Attention Networks for Action Recognition and Detection
Li, J., Liu, X., Zhang, W., Zhang, M., Song, J., & Sebe, N. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2965434 · Cited by 181 · see citing works
Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs
Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2791180 · Cited by 178 · see citing works
Self-citationA Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences
Asadi, M., Clapés, A., Bellantonio, M., Escalante, H. J., Ponce-López, V., Baró, X., Guyon, I., Shohreh Kasaei, B., & Escalera, S. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.150 · Cited by 176 · see citing works
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
Wu, C.-Y., Li, Y., Mangalam, K., Fan, H., Xiong, B., Malik, J., & Feichtenhofer, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01322 · Cited by 176 · see citing works
Deep Sequential Context Networks for Action Prediction
Kong, Y., Tao, Z., & Fu, Y. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.390 · Cited by 174 · see citing works
Recent advances in video-based human action recognition using deep learning: A review
Wu, D., Sharma, N., & Blumenstein, M. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966210 · Cited by 173 · see citing works
Learning to Score Figure Skating Sport Videos
Xu, C., Fu, Y., Zhang, B., Chen, Z., Jiang, Y., & Xue, X. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2927118 · Cited by 173 · see citing works
Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification
Wu, Z., Jiang, Y., Wang, X., Ye, H., & Xue, X. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2964328 · Cited by 172 · see citing works
A Comprehensive Study of Deep Video Action RecognitionPreprint
Zhu, Y., Li, X., Liu, C., Zolfaghari, M., Xiong, Y., Wu, C., Zhang, Z., Tighe, J., Manmatha, R., & Li, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06567 · Cited by 172 · see citing works
RNN Fisher Vectors for Action Recognition and Image Annotation
Lev, G., Sadeh, G., Klein, B., & Wolf, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_50 · Cited by 170 · see citing works
Kar, A., Rai, N., Sikka, K., & Sharma, G. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.604 · Cited by 170 · see citing works
Regularizing Long Short Term Memory with 3D Human-Skeleton Sequences for Action Recognition
Mahasseni, B., & Todorović, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.333 · Cited by 169 · see citing works
Encouraging LSTMs to Anticipate Actions Very Early
Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.39 · Cited by 169 · see citing works
- Authors
- Wang, L., Qiao, Y., & Tang, X.
- Affiliations
- Chinese University of Hong Kong (HK)
- Chinese Academy of Sciences (CN)
- ORCID
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 4305-4314
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1944615693
- Cited by
- 1,194
- References
- 49
- Citations by year
- 2026: 15 · 2025: 40 · 2024: 47 · 2023: 74 · 2022: 74 · 2021: 111 · 2020: 129 · 2019: 192 · 2018: 202 · 2017: 186 · 2016: 108 · 2015: 16
- FWCI
- 70.00
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Keywords
- human action recognition, video representation, channel normalization, handcrafted features
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
