Corpus ID: 67855445

Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos

@article{Hu2019ProgressRR,
  title={Progress Regression RNN for Online Spatial-Temporal Action Localization in Unconstrained Videos},
  author={Bo Hu and Jianfei Cai and Tat-Jen Cham and Junsong Yuan},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.00304}
}
  • Bo Hu, Jianfei Cai, +1 author Junsong Yuan
  • Published in ArXiv 2019
  • Computer Science
  • Previous spatial-temporal action localization methods commonly follow the pipeline of object detection to estimate bounding boxes and labels of actions. However, the temporal relation of an action has not been fully explored. In this paper, we propose an end-to-end Progress Regression Recurrent Neural Network (PR-RNN) for online spatial-temporal action localization, which learns to infer the action by temporal progress regression. Two new action attributes, called progression and progress rate… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 53 REFERENCES

    Action Tubelet Detector for Spatio-Temporal Action Localization

    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL

    Thumos challenge 2013

    • Y. Jiang, J. Liu, +4 authors R. Sukthankar
    • Center for Research in Computer Vision, UCF, 2014.
    • 2014
    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    YOLO9000: Better, Faster, Stronger

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL

    SSD: Single Shot MultiBox Detector

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL