Enhancing Human Action Recognition in Videos through Dense-Level Features Extraction and Optimized Long Short-Term Memory
Author:
Affiliation:
1. The University of Aizu,Dept. of computer science and engineering,Aizuwakamatsu,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10624580/10624586/10624624.pdf?arnumber=10624624
Reference37 articles.
1. Action recognition in surveillance video using convents and motion history images;Luo
2. Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking;Liu
3. Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
4. Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM
5. A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications
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