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Ordered trajectories for human action recognition with large number of classes

  • Published:2015-10 Issue: Volume:42 Page:22-34
  • ISSN:0262-8856
  • Container-title:Image and Vision Computing
  • language:en
  • Short-container-title:Image and Vision Computing

Author:

Ramana Murthy O.V.,Goecke Roland

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing

Reference53 articles.

1. An iterative image registration technique with an application to stereo vision;Lucas,1981

2. Action recognition by dense trajectories;Wang,2011

3. Better exploiting motion for better action recognition;Jain,2013

4. Trajectory-based modeling of human actions with motion reference points;Jiang,2012

5. Motion interchange patterns for action recognition in unconstrained videos;Kliper-Gross,2012

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1. SDIGRU: Spatial and Deep Features Integration Using Multilayer Gated Recurrent Unit for Human Activity Recognition;IEEE Transactions on Computational Social Systems;2024-02

2. Human Action Recognition by Deep Learning Technique with Multiple-Searching Genetic Algorithm;2023 27th International Computer Science and Engineering Conference (ICSEC);2023-09-14

3. LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques;Applied Computational Intelligence and Soft Computing;2022-08-23

4. IoT‐Assisted Smart Device for Blind People;Intelligent Systems for Rehabilitation Engineering;2022-01-16

5. LTC-SUM: Lightweight Client-Driven Personalized Video Summarization Framework Using 2D CNN;IEEE Access;2022
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