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Multi-Temporal Depth Motion Maps-Based Local Binary Patterns for 3-D Human Action Recognition.

, , , , , and . IEEE Access, (2017)

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Depth Context: a new descriptor for human activity recognition by using sole depth sequences., and . Neurocomputing, (2016)Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition., , and . CoRR, (2017)Combining Adaptive Hierarchical Depth Motion Maps With Skeletal Joints for Human Action Recognition., , , and . IEEE Access, (2019)A Survey on 3D Skeleton-Based Action Recognition Using Learning Method., , , and . CoRR, (2020)LTE-WLAN Integrated Virtualization Network Architecture., , , and . 5GWN, volume 211 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 517-528. Springer, (2017)Action recognition from depth sequences using weighted fusion of 2D and 3D auto-correlation of gradients features., , , , , and . Multim. Tools Appl., 76 (3): 4651-4669 (2017)Human activity prediction by mapping grouplets to recurrent Self-Organizing Map., , , and . Neurocomputing, (2016)TFAN: Twin-Flow Axis Normalization for Human Motion Prediction., , , , and . IEEE Signal Process. Lett., (2024)A Mixer Layer is Worth One Graph Convolution: Unifying MLP-Mixers and GCNs for Human Motion Prediction., , , and . CoRR, (2023)Explore Human Parsing Modality for Action Recognition., , , , , , and . CoRR, (2024)