Technology of Graphic & Image
|
3848-3853

Action recognition method with feature sampling and motion information enhancement

Luo Huilan
Bao Zhongsheng
School of Information Engineering, Jiangxi University of Technology, Ganzhou Jiangxi 341000, China

Abstract

Based on deep models, video action recognition typically involves sampling the input video and then extracting features from the obtained video frames to classify actions. Therefore, the video frame sampling method directly affects the effectiveness of action recognition. Aiming to sample key and effective features while enhanced the motion information in videos, this paper proposed a LGMeNet based on a feature-level sampling strategy. Firstly, it used a feature-level sampling module to uniformly select frames with the same motion information from the input data. Secondly, it employed a local motion feature extraction module to compute short-term motion features using a similarity function. Finally, it utilized a LSTM network in the global motion feature extraction module to calculate multi-scale long-term motion features. Experimental evaluations show that LGMeNet achieves accuracies of 97.7% and 56.9% on the UCF101 and Something-SomethingV1 datasets, respectively. The results of this study demonstrate the effectiveness of LGMeNet in enhancing action recognition and highlight its significance for further advancements in related research areas.

Foundation Support

国家自然科学基金资助项目(61862031)
江西省主要学科技术带头人领军人才计划资助项目(20213BCJ22004)
江西省学位与研究生教育教学改革研究重点项目(JXYJG-2020-120)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0216
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Technology of Graphic & Image
Pages: 3848-3853
Serial Number: 1001-3695(2023)12-052-3848-06

Publish History

[2023-07-25] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

罗会兰, 包中生. 特征采样运动信息增强的动作识别方法 [J]. 计算机应用研究, 2023, 40 (12): 3848-3853. (Luo Huilan, Bao Zhongsheng. Action recognition method with feature sampling and motion information enhancement [J]. Application Research of Computers, 2023, 40 (12): 3848-3853. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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