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Titlebook: Artificial Intelligence and Robotics; 7th International Sy Shuo Yang,Huimin Lu Conference proceedings 2022 The Editor(s) (if applicable) an

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Learning Visual Tempo for Action Recognition,cy of action recognition by learning more visual tempo of different actions. Extensive evaluations show that MRF-ST reaches the state-of-the-art on the UCF-101 and HMDB-51 datasets. Further analysis also indicates that MRF-ST can significantly improve the performance at the scenes with large varianc
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Deep Learning-Based Virtual Trajectory Generation Scheme,ile for the real trajectory, the false positive rate is only 6.5%. The virtual trajectory generated by the proposed scheme has human motion patterns similar to the real trajectory, and protects against colluding, inference, and channel attacks. The generated virtual trajectory points will not be dis
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