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

Kumar, Rahul; Kumar, Shailender

doi:10.1117/1.oe.62.2.023102

Cited by 6 publications

(5 citation statements)

References 38 publications

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“…As HAR technologies continue to progress, their integration with AI is expected to result in more sophisticated surveillance systems. This integration enhances the efficiency of both action detection and contextual interpretation, aligning surveillance operations more closely with real-world requirements [32], [40], [41].…”

Section: ) Har In Surveillancementioning

confidence: 98%

Human Action Recognition Systems: A Review of the Trends and State-of-the-Art

Karim,

Khalid,

Aleryani

et al. 2024

IEEE Access

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Human action recognition (HAR), deeply rooted in computer vision, video surveillance, automated observation, and human computer interaction (HCI), enables precise identification of human actions. Numerous research groups have dedicated their efforts to various applications and problem domains in HAR systems. They trained classification models using diverse datasets, enhanced hardware capabilities, and employed different metrics to assess performance. Although several surveys and review articles have been published periodically to highlight research advancements in HAR, there is currently no comprehensive and up-to-date study that encompasses architecture, application areas, techniques/algorithms, and evaluation methods as well as challenges and issues. To bridge this gap in the literature, this article presents a comprehensive analysis of the current state of HAR systems by thoroughly examining a meticulously chosen collection of 136 publications published within the past two decades. These findings have significant implications for researchers engaged in different aspects of HAR systems.

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Section: ) Har In Surveillancementioning

confidence: 98%

Human Action Recognition Systems: A Review of the Trends and State-of-the-Art

Karim,

Khalid,

Aleryani

et al. 2024

IEEE Access

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“…In recent years, specialized automatic surveillance systems tailored for video action recognition have significantly progressed in handling challenges [18,19]. Artificial intelligence-based human action recognition in video sequences has advanced significantly, addressing challenges like detecting abnormal events and recognizing diverse activities in complex scenes using machine learning and deep learning [20].…”

Section: Related Workmentioning

confidence: 99%

A State-Based Language for Enhanced Video Surveillance Modeling (SEL)

Ramirez-Rosales,

Diaz-Jimenez,

Canton-Enriquez

et al. 2024

Modelling

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SEL, a State-based Language for Video Surveillance Modeling, is a formal language designed to represent and identify activities in surveillance systems through scenario semantics and the creation of motion primitives structured in programs. Motion primitives represent the temporal evolution of motion evidence. They are the most basic motion structures detected as motion evidence, including operators such as sequence, parallel, and concurrency, which indicate trajectory evolution, simultaneity, and synchronization. SEL is a very expressive language that characterizes interactions by describing the relationships between motion primitives. These interactions determine the scenario’s activity and meaning. An experimental model is constructed to demonstrate the value of SEL, incorporating challenging activities in surveillance systems. This approach assesses the language’s suitability for describing complicated tasks.

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“…For the further exploration of RGB-D action recognition techniques, the surveys [32,45,46] and the review in [47] offer detailed insights into the field's current state and future directions.…”

Section: Machine Learning-based Solutionsmentioning

confidence: 99%

Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites

Amel,

Siebert,

Mahmoudi

2024

Electronics

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The growing demand for advanced tools to ensure safety in railway construction projects highlights the need for systems that can smoothly integrate and analyze multiple data modalities, such as multimodal learning algorithms. The latter, inspired by the human brain’s ability to integrate many sensory inputs, has emerged as a promising field in artificial intelligence. In light of this, there has been a rise in research on multimodal fusion approaches, which have the potential to outperform standard unimodal solutions. However, the integration of multiple data sources presents significant challenges to be addressed. This work attempts to apply multimodal learning to detect dangerous actions using RGB-D inputs. The key contributions include the evaluation of various fusion strategies and modality encoders, as well as identifying the most effective methods for capturing complex cross-modal interactions. The superior performance of the MultConcat multimodal fusion method was demonstrated, achieving an accuracy of 89.3%. Results also underscore the critical need for robust modality encoders and advanced fusion techniques to outperform unimodal solutions.

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

Cited by 6 publications

References 38 publications

Human Action Recognition Systems: A Review of the Trends and State-of-the-Art

Human Action Recognition Systems: A Review of the Trends and State-of-the-Art

A State-Based Language for Enhanced Video Surveillance Modeling (SEL)

Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites

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