On Space-Time Interest Points

Object recognition from local scale-invariant features

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A Combined Corner and Edge Detector

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CONDENSATION—Conditional Density Propagation for Visual Tracking

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Feature detection with automatic scale selection

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Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons

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Large-Scale Video Classification with Convolutional Neural Networks

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Hmdb: a large video database for human motion recognition

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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

ViViT: A Video Vision Transformer

Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00676 · Cited by 2,767

Dense Trajectories and Motion Boundary Descriptors for Action Recognition

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Action recognition with trajectory-pooled deep-convolutional descriptors

Wang, L., Qiao, Y., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299059 · Cited by 1,194

A Low Power, Fully Event-Based Gesture Recognition System

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Temporal Action Detection with Structured Segment Networks

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A database for fine grained activity detection of cooking activities

Rohrbach, M., Amin, S., Andriluka, M., & Schiele, B. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247801 · Cited by 521

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

TURN 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

Combining embedded accelerometers with computer vision for recognizing food preparation activities

Stein, S., & McKenna, S. J. (2013) · Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing · doi:10.1145/2493432.2493482 · Cited by 448

Understanding egocentric activities

Fathi, A., Farhadi, A., & Rehg, J. M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126269 · Cited by 384

A survey of depth and inertial sensor fusion for human action recognition

Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356

Automatic annotation of human actions in video

Duchenne, O., Laptev, I., Sivic, J., Bach, F., & Ponce, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459279 · Cited by 275

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

Steven Eyobu, O., & Han, D. S. (2018) · Sensors · doi:10.3390/s18092892 · Cited by 259

Attention-Based 3D-CNNs for Large-Vocabulary Sign Language Recognition

Huang, J., Zhou, W., Li, H., & Li, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870740 · Cited by 232

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

What Would You Expect? Anticipating Egocentric Actions With Rolling-Unrolling LSTMs and Modality Attention

Furnari, A., & Farinella, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00635 · Cited by 182

Super Normal Vector for Human Activity Recognition with Depth Cameras

Yang, X., & Tian, Y. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2565479 · Cited by 162

MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

Jain, A., Tompson, J., LeCun, Y., & Bregler, C. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16808-1_21 · Cited by 142

Video and accelerometer-based motion analysis for automated surgical skills assessment

Zia, A., Sharma, Y., Bettadapura, V., Sarin, E. L., & Essa, I. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1704-z · Cited by 134

Weakly supervised learning of actions from transcripts

Kuehne, H., Richard, A., & Gall, J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.06.004 · Cited by 119

Untitled work

(2016) · MPG.PuRe (Max Planck Society) · Cited by 107

Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

Jain, V., Crowley, J. L., Dey, A. K., & Lux, A. (2014) · Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge · doi:10.1145/2661806.2661817 · Cited by 78

Compressive Sequential Learning for Action Similarity Labeling

Qin, J., Liu, L., Zhang, Z., Wang, Y., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2508600 · Cited by 55

Event-based media processing and analysis: A survey of the literature

Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49

Steps Towards a Theory of Visual Information: Active Perception, Signal-to-Symbol Conversion and the Interplay Between Sensing and Control

Soatto, S. (2011) · arXiv · doi:10.48550/arxiv.1110.2053 · Cited by 40

Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends

Ezzat, M. A., Abd El Ghany, M. A., Almotairi, S., & Salem, M. A.-M. (2021) · Sensors · doi:10.3390/s21093222 · Cited by 39

Recognising complex activities with histograms of relative tracklets

Stein, S., & McKenna, S. J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.08.012 · Cited by 11