  Human Action Recognition (HAR) encompasses the task of monitoring human
activities across various domains, including but not limited to medical,
educational, entertainment, visual surveillance, video retrieval, and the
identification of anomalous activities. Over the past decade, the field of HAR
has witnessed substantial progress by leveraging Convolutional Neural Networks
(CNNs) to effectively extract and comprehend intricate information, thereby
enhancing the overall performance of HAR systems. Recently, the domain of
computer vision has witnessed the emergence of Vision Transformers (ViTs) as a
potent solution. The efficacy of transformer architecture has been validated
beyond the confines of image analysis, extending their applicability to diverse
video-related tasks. Notably, within this landscape, the research community has
shown keen interest in HAR, acknowledging its manifold utility and widespread
adoption across various domains. This article aims to present an encompassing
survey that focuses on CNNs and the evolution of Recurrent Neural Networks
(RNNs) to ViTs given their importance in the domain of HAR. By conducting a
thorough examination of existing literature and exploring emerging trends, this
study undertakes a critical analysis and synthesis of the accumulated knowledge
in this field. Additionally, it investigates the ongoing efforts to develop
hybrid approaches. Following this direction, this article presents a novel
hybrid model that seeks to integrate the inherent strengths of CNNs and ViTs.
