A Human Activity Recognition Method using the Maximum Optical Flow based Feature Bounding Box-Reference-Cited by-同舟云学术

A Human Activity Recognition Method using the Maximum Optical Flow based Feature Bounding Box

Published:2018-02-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 10th International Conference on Machine Learning and Computing
language:
Short-container-title:

Author:

Sang Shen¹,Huang Zhibin¹,Kang Zilu²

Affiliation:

1. Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, School of Computer Science, BUPT, Beijing, China

2. Institute of Internet of Things Information Sciences Academy, CETC, Beijing, China

Funder

China Postdoctoral Science Foundation funded projects

Fundamental Research Funds for the Central Universities

State Key Laboratory of Software Development Environment

2015 IBM SUR Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3195106.3195141

Reference15 articles.

1. A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition;Sargano Allah B.;J. Applied Sciences,2017

2. A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector

3. An Approach to Pose-Based Action Recognition

4. Hybrid classifier based human activity recognition using the silhouette and cells

5. Recognizing human actions using novel space-time volume binary patterns

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An optimization high-resolution network for human pose recognition based on attention mechanism;Multimedia Tools and Applications;2023-10-23

2. Chaotic whale-atom search optimization-based deep stacked auto encoder for crowd behaviour recognition;Journal of Experimental & Theoretical Artificial Intelligence;2022-06-15