Human action recognition in videos based on spatiotemporal features and bag-of-poses

Silva, Murilo Varges da; Marana, Aparecido Nilceu

doi:10.1016/j.asoc.2020.106513

Cited by 20 publications

(6 citation statements)

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“…The team proposes an improved method based on existing human pose extraction, which maps each segment of a 2D pose to a point to extract spatial features. The results of the study indicate that the method is effective in improving the recognition rate [10].…”

Section: Literature Reviewmentioning

confidence: 84%

Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement Recognition

Zhang

2024

Preprint

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With the development of computers in recent years, human body recognition technology has been vigorously developed and is widely used in motion analysis, video surveillance and other fields. As the traditional human action recognition relies on video decomposition frame-by-frame, artificially designed motion features to achieve the role of recognition, this approach is both energy-consuming recognition efficiency is also very low. Thanks to the advent of deep learning, computers can automatically extract features from movements and then recognize and classify them. This research is based on deep learning to improve human pose estimation. Firstly, Involution's feature extraction network is proposed for lightweight human pose estimation, which is combined with existing human pose estimation models to recognize human pose. Each joint of the human body is labelled and classified, weights are added to each part, features are extracted between the joints at each moment, and the extracted features are then fed into a long and short term memory neural network for recognition. Experimental results show that the number of parameters and computational effort of the improved human pose estimation model is reduced by around 40% compared to the original model, while still providing a slight improvement in accuracy. The performance of the model under each algorithm is compared with the model proposed in this study, and the results show that the proposed model has better performance in recognizing different martial arts movements.

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Section: Literature Reviewmentioning

confidence: 84%

Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement Recognition

Zhang

2024

Preprint

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“…Silva and Marana 22 recommended a spatiotemporal attribute as well as bag-of-poses aimed at HAR. In this paper, a methodology of depicting 2D poses was presented.…”

Section: Har: a Comprehensive Approachmentioning

confidence: 99%

Survey on artificial intelligence-based human action recognition in video sequences

Kumar

2023

Opt. Eng.

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In the artificial intelligence domain, human action recognition (HAR) has evolved as one of the major active research topics as a reason of diverse applications, namely video surveillance. The extensive variation types amidst routine human activities make the recognition procedure much more intricate. As a reason for an increment in the cameras' usage, automated systems are essential to categorize activities, such as these utilizing computationally intelligent methodologies, namely machine learning and deep learning (DL). This paper's organization covers all the characteristics of the HAR's general framework. This work talks about the public dataset's characteristics utilized that are aimed at HAR. After that, it summarizes and classifies the recently published research enhancement under the common framework. The merits and demerits of dimensionality reduction, action representation, and as well as action analysis methodologies are offered. After that, the outcomes exhibit the DL methodologies' performance via graphs. This article gives a clear vision of what we can explore in the activity recognition area. The work talks about HAR's challenges and its future directions.

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“…In spite of the fact that these maps have been calculated to discover local maximal that offer one or more ridge data inside of binary edges for ridge data production. [62].…”

Section: ) Ridgementioning

confidence: 99%

Depth Sensors-Based Action Recognition Using a Modified K-Ary Entropy Classifier

et al. 2023

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Human action recognition in videos based on spatiotemporal features and bag-of-poses

Cited by 20 publications

References 20 publications

Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement Recognition

Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement Recognition

Survey on artificial intelligence-based human action recognition in video sequences

Depth Sensors-Based Action Recognition Using a Modified K-Ary Entropy Classifier

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