Video-based Human Action Recognition: Analyzing techniques for recognizing human actions and activities in videos, including temporal modeling and motion analysis
Keywords:
Video-based, Human Action RecognitionAbstract
Video-based human action recognition is a crucial task in computer vision with applications in surveillance, human-computer interaction, and video indexing. This paper provides a comprehensive review of techniques for recognizing human actions and activities in videos. We focus on temporal modeling and motion analysis, which are key components in achieving high recognition accuracy.
We begin by discussing the challenges of human action recognition, including variability in human movements, occlusions, and complex interactions between multiple individuals. We then review traditional approaches such as optical flow-based methods and discuss their limitations in handling complex actions and scenes.
Next, we delve into deep learning techniques, which have shown remarkable success in human action recognition. We review popular deep learning architectures such as 3D Convolutional Neural Networks (CNNs) and Temporal Convolutional Networks (TCNs) and discuss their effectiveness in capturing temporal information and modeling complex motion patterns.
Furthermore, we explore the integration of attention mechanisms and spatial-temporal graph networks for improved action recognition performance. We also discuss the importance of large-scale datasets such as UCF101 and HMDB51 in training deep learning models for human action recognition.
Finally, we highlight future research directions, including the use of generative adversarial networks (GANs) for data augmentation and the integration of multimodal data (e.g., RGB and depth) for more robust action recognition.
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References
Prabhod, Kummaragunta Joel. "ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES IN IMPROVING PRODUCTION SYSTEMS." Science, Technology and Development 10.7 (2021): 698-707.
Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.
Tatineni, Sumanth, and Karthik Allam. "Implementing AI-Enhanced Continuous Testing in DevOps Pipelines: Strategies for Automated Test Generation, Execution, and Analysis." Blockchain Technology and Distributed Systems 2.1 (2022): 46-81.
Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
Perumalsamy, Jegatheeswari, Chandrashekar Althati, and Lavanya Shanmugam. "Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy." Journal of Artificial Intelligence Research 2.2 (2022): 51-82.
Devan, Munivel, Lavanya Shanmugam, and Chandrashekar Althati. "Overcoming Data Migration Challenges to Cloud Using AI and Machine Learning: Techniques, Tools, and Best Practices." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 1-39.
Althati, Chandrashekar, Bhavani Krothapalli, and Bhargav Kumar Konidena. "Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 38-79.
Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "A Comparative Analysis of Lightweight Cryptographic Protocols for Enhanced Communication Security in Resource-Constrained Internet of Things (IoT) Environments." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 121-142.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
