## 2 Related Works
It includes synchronized video and motion capture data, enabling accurate ground truth annotations for human key point positions. Another noteworthy dataset in the current literature that is specifically designed for human gait movements includes HumanEva [40], Gait3D [41], and CASIA [42]. HumanEva [40] consists of synchronized grayscale and color video sequences with corresponding 3D body poses captured from a motion capture system, featuring four subjectsperforming six common actions, accompanied by error metrics for evaluating 2D and 3D pose accuracy, as well as separate training, validation, and testing sets. Gait3D comprises 4000 individuals and encompasses over 25,000 sequences obtained from 39 cameras capturing an unconstrained indoor environment [41]. CASIA, a collection of subsets, notably CASIA-A and CASIA-B, offers gait data from diverse walking scenarios and viewpoints [42]. It includes RGB videos and depth maps, providing visual and depth information for gait analysis. These extensive datasets have sparked significant advancements in 2D pose estimation, resulting in modern motion capture techniques achieving an average error of approximately 20 mm per joint [43]. However, many of these datasets consist of recordings of random images of human actions and may not accurately represent normal human gait. These factors can impact the accuracy of gait recognition models, highlighting the need for improvement in practical applications. Gait-specific datasets like GAIT3D and CASIA, although valuable, still leave room for enhancing the performance of trained frameworks. In a study by [PERSON] et al. [4], various deep learning models trained with these datasets were explored and discussed in detail. The study compiled several dedicated articles and summarized current advancements in deep learning-based 2D and 3D human pose estimation and concluded that the field suffers limitations of the dedicated database involving complex human movements. Therefore, there is a need to address this gap by developing a comprehensive dataset that includes high-resolution data from various subjects performing their normal gait. Thus, the current study provides a comprehensive high-resolution dataset that was specially prepared for gait analysis using high-resolution data captured using the VICON motion capture system. This proposed dataset can be used to train different deep-learning models for creating a 2D estimator capable of efficiently predicting the 2D key joint position of the person from 2D images. Furthermore, it is also equally important to consider the choice of deep learning platforms for training the model with the dataset, as well as for model inference and deployment. The selection of the appropriate deep learning platform plays a crucial role in the success of biomedical research applications. These platforms provide flexibility to be fine-tuned with custom datasets to improve accuracy and adapt to specific domains or enhance performance. The results achieved by testing various deep learning models such as OpenPose, MediaPipe, AlphaPose, and HRNet trained with different state-of-the-art datasets have been widely discussed in the literature [5; 13; 20; 34; 41; 42]. Recently, a study by [PERSON], et al. [44] has shown the mainstream and milestone approaches for these human body presentations since the year 2014 under unified frameworks. In particular, it provides insightful analyses for the intrinsic connections and methods evolution from 2D to 3D pose estimation and analyze the solutions for challenging cases, such as the lack of data, the inherent ambiguity between 2D and 3D, and the complex multi-person scenarios. Next, it summarizes the benchmarks, evaluation metrics, and the quantitative performance of popular approaches. Finally, it discusses the challenges and provides the deep thinking of promising directions for future research. We believe this survey will provide the readers (researchers, engineers, developers, etc.) with a deep and insightful understanding of monocular human pose estimation.