A Detail Study of Support Vector Machine, K-Nearest Neighbours and Convolutional Neural Network for Human Action Recognition in Videos
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Abstract
Over the previous span, Human Activity Recognition (HAR) has evolved into a critical research area within the computer vision, driven by advancements in video-based action recognition techniques. Unlike image-based methods, video-based HAR leverages Spatial and Temporal information, offering a richer understanding of human behaviors. This area has found several applications in diverse domains, including education, intelligent surveillance, healthcare, entertainment, and autonomous systems. The cameras and sensing devices: There is an increasing demand for automated HAR systems utilizing computationally intelligent methods such as Deep learning (DL) and Machine Learning (ML). This paper delivers a detail study of DL and ML techniques applied to HAR between 2014 and 2025. It explores various modalities used for action recognition, including RGB-D cameras, audio, and inertial sensors, and examines their roles in enhancing HAR performance. A detailed analysis of public datasets is presented, highlighting their characteristics, strengths, and limitations. Additionally, this survey explores into how action representation, dimensionality reduction, and actually action analysis methods, identifying their respective advantages and drawbacks. In this paper discusses applications of HAR, including human-computer interaction, remote health monitoring, virtual reality, and abnormal behavior detection, emphasizing its transformative impact on these fields. Key challenges, such as scalability, real-time processing, and environmental variability, are outlined, along with the future research directions aimed at developing robust and efficient HAR systems. This survey serves as a valuable resource for researchers and practitioners, providing insights into the state-of-the-art techniques and to make it easier for further advancements in HAR.