Human action recognition using fusion of multiview and deep features: an application to video surveillance

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

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

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · 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.1993.341010 · Cited by 14,474 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · 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

An empirical study of the naive Bayes classifier

Rish, I. (2001) · Cited by 2,532 · see citing works

Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition

Rodriguez, M. D., Ahmed, J., & Shah, M. (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.2008.4587727 · Cited by 1,258 · see citing works

Recognizing realistic actions from videos “in the wild”

Liu, J., Luo, J., & Shah, M. A. (2009) · 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.2009.5206744 · Cited by 1,089 · see citing works

DenseNet: Implementing Efficient ConvNet Descriptor PyramidsPreprint

Iandola, F., Moskewicz, M. W., Karayev, S., Girshick, R., Darrell, T., & Keutzer, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.1869 · Cited by 909 · see citing works

Free viewpoint action recognition using motion history volumes

Weinland, D., Ronfard, R., & Boyer, E. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.07.013 · Cited by 906 · see citing works

Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

Zhang, P., Lan, C., Zeng, W.-J., Xing, J., Xue, J.-R., & Zheng, N. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr42600.2020.00119 · Cited by 564 · see citing works

View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition

Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2896631 · Cited by 523 · see citing works

A 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 · cited 1× · see citing works

Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition

Liu, A.-A., Su, Y., Nie, W., & Kankanhalli, M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2537337 · Cited by 375 · 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

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

Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures

Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., & Feng, D. D. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2850149 · Cited by 225 · see citing works

Human action recognition using two-stream attention based LSTM networks

Dai, C., Liu, X., & Lai, J. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105820 · Cited by 219 · see citing works

Asymmetric 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

An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection

Khan, M. A., Lali, M. I. U., Sharif, M. I., Javed, K., Aurangzeb, K., Haider, S. I., Altamrah, A. S., & Akram, T. (2019) · IEEE Access · doi:10.1109/access.2019.2908040 · Cited by 202 · see citing works

A framework for offline signature verification system: Best features selection approach

Sharif, M. I., Khan, M. A., Faisal, M. S., Yasmin, M., & Fernandes, S. L. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.01.021 · Cited by 191 · see citing works

Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments

Ullah, A., Muhammad, K., Haq, I. U., & Baik, S. W. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.01.029 · Cited by 185 · see citing works

Hand-crafted and deep convolutional neural network features fusion and selection strategy: An application to intelligent human action recognition

Khan, M. A., Sharif, M., Akram, T., Raza, M., Saba, T., & Rehman, A. U. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105986 · Cited by 155 · see citing works

Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features

Rashid, M., Khan, M. A., Sharif, M. I., Raza, M., Sarfraz, M. M., & Afza, F. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-7031-0 · Cited by 115 · see citing works

Exploiting deep residual networks for human action recognition from skeletal data

Pham, H., Khoudour, L., Crouzil, A., Zegers, P., & Velastín, S. A. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.03.003 · Cited by 80 · see citing works

Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution

Arshad, H., Khan, M. A., Sharif, M., et al. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-00947-0 · Cited by 75 · see citing works

An Effective Framework for Driver Fatigue Recognition Based on Intelligent Facial Expressions Analysis

Khan, S. A., Hussain, S., Sun, X., & Yang, S. (2018) · IEEE Access · doi:10.1109/access.2018.2878601 · Cited by 67 · see citing works

Depth Maps-Based Human Segmentation and Action Recognition Using Full-Body Plus Body Color Cues Via Recognizer Engine

Jalal, A., Kamal, S., & Azurdia-Meza, C. A. (2019) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-018-00012-w · Cited by 67 · see citing works

Bayesian Hierarchical Dynamic Model for Human Action Recognition

Zhao, R., Xu, W., Su, H., & Ji, Q. (2019) · 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.2019.00792 · Cited by 67 · see citing works

Action Recognition From Arbitrary Views Using Transferable Dictionary Learning

Zhang, J., Shum, H. P. H., Han, J., & Shao, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2836323 · Cited by 66 · see citing works

A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition

Sharif, M. I., Attique, M., Tahir, M. Z., Yasmim, M., Saba, T., & Tanik, U. J. (2019) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2020040104 · Cited by 60 · see citing works

Human action recognition: a framework of statistical weighted segmentation and rank correlation-based selection

Sharif, M., Khan, M. A., Zahid, F., et al. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-019-00789-0 · Cited by 59 · see citing works

Human Behavior Analysis Based on Multi-Types Features Fusion and Von Nauman Entropy Based Features Reduction

Aurangzeb, K., Haider, I., Khan, M. A., et al. (2019) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2019.2611 · Cited by 39 · see citing works

Improved strategy for human action recognition; experiencing a cascaded design

Khan, M. A., Akram, T., Sharif, M., Muhammad, N., Javed, M. Y., & Naqvi, S. R. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.5769 · Cited by 35 · see citing works

Multilinear Principal Component Analysis Network for Tensor Object Classification

Wu, J., Qiu, S., Zeng, R., Kong, Y., Senhadji, L., & Shu, H. (2017) · IEEE Access · doi:10.1109/access.2017.2675478 · Cited by 34 · see citing works

Human action recognition using bag of global and local Zernike moment features

Aly, S., & Sayed, A. Z. E. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7674-5 · Cited by 29 · see citing works

Order-aware convolutional pooling for video based action recognition

Wang, P., Liu, L., Shen, C., & Shen, H. T. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.03.002 · Cited by 29 · see citing works

Action recognition based on binary patterns of action-history and histogram of oriented gradient

Ahad, M. A. R., Islam, N., & Jahan, I. (2016) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-016-0229-4 · Cited by 28 · see citing works

Video spatiotemporal mapping for human action recognition by convolutional neural network

Zare, A., Moghaddam, H. A., & Sharifi, A. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-019-00788-1 · Cited by 25 · see citing works

Human action recognition: a construction of codebook by discriminative features selection approach

Siddiqui, S., Khan, M. A., Bashir, K., et al. (2018) · International Journal of Applied Pattern Recognition · doi:10.1504/ijapr.2018.094815 · Cited by 24 · see citing works

Human action recognition based on the Grassmann multi-graph embedding

Rahimi, S., Aghagolzadeh, A., & Ezoji, M. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1354-1 · Cited by 16 · see citing works

Human action recognition: a construction of codebook by discriminative features selection approach

Siddiqui, S., Azam, F., Bashir, K., et al. (2018) · International Journal of Applied Pattern Recognition · doi:10.1504/ijapr.2018.10016096 · Cited by 13 · see citing works

Human Action Recognition and Assessment Via Deep Neural Network Self-Organization

Parisi, G. I. (2020) · Springer eBooks · doi:10.1007/978-3-030-46732-6_10 · Cited by 13 · see citing works

An effective human action recognition system based on Zernike moment features

Aly, S., & Sayed, A. Z. E. (2019) · doi:10.1109/itce.2019.8646504 · Cited by 10 · see citing works

Human Action Recognition Based on Fusion Features

Yang, S., Yang, J., Li, F., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-15235-2_81 · Cited by 4 · see citing works

Facial Expression Recognition in Unconstrained Environment

Khan, S. A. (2019) · Cited by 1 · see citing works

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works

Pose Estimation and Detection for Event Recognition using Sense-Aware Features and Adaboost Classifier

Akhter, I., Jalal, A., & Kim, K. (2021) · 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST) · doi:10.1109/ibcast51254.2021.9393293 · Cited by 58 · see citing works

Weighted and deterministic entropy measure for image registration using mutual information

Rodriguez-Carranza, C. E., & Loew, M. H. (1998) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.310854 · Cited by 35 · see citing works

Brain Image Registration Based on Entropy of Mutual Information Matrix

Liu, C., Hu, S., Gu, J. J., et al. (2007) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2007.295 · Cited by 6 · see citing works

A Text Feature Selection Method Using the Improved Mutual Information and Information Entropy

Cheng, W.-Q. (2013) · Journal of Nanjing University of Posts and Telecommunications · Cited by 2 · see citing works

A bound on mutual information for image registration

Skouson, M. B., Guo, Q., & Liang, Z. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.938252 · Cited by 42 · see citing works

Tsallis and Renyi's embedded entropy based mutual information for multimodal image registration

Sahoo, S., Nanda, P. K., & Samant, S. (2013) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2013.6776207 · Cited by 5 · see citing works