A Review on Computer Vision-Based Methods for Human Action Recognition.

Area of Science:

• Computer Vision
• Artificial Intelligence

Background:

• Human action recognition is crucial for applications like surveillance and healthcare.
• Accurate vision-based recognition remains a significant challenge in computer vision research.

Purpose of the Study:

• To review recent advancements in human action recognition systems.
• To categorize and analyze state-of-the-art methods and datasets.
• To provide insights for future research directions.

Main Methods:

• Categorization of methods from hand-crafted representations (holistic, local) to deep learning (discriminative, generative, multi-modality).
• Presentation and analysis of common human action recognition datasets.
• Comparative analysis of different approaches and data sources.

Main Results:

• Identification of trends shifting from traditional feature engineering to deep learning models.
• Overview of diverse datasets enabling robust model training and evaluation.
• Synthesis of current research landscape and performance benchmarks.

Conclusions:

• Deep learning and multi-modality approaches show significant promise for improved action recognition.
• Standardized datasets and rigorous evaluation are key for progress.
• Future research should focus on addressing complex actions and real-world variability.