This paper presents a method for human pose estimation using Deep Neural Networks (DNNs). The approach formulates pose estimation as a DNN-based regression problem, where the goal is to predict the coordinates of body joints. A cascade of DNN regressors is used to achieve high precision in joint localization. The method is holistic, meaning it considers the entire body context rather than focusing on individual parts. The DNN-based approach is simple yet powerful, leveraging recent advances in deep learning. The method is evaluated on four academic benchmarks, achieving state-of-the-art or better performance. The key idea is to use a convolutional DNN to regress the positions of body joints based on the full image. This allows the model to capture the full context of each joint, leading to more accurate predictions. The DNN is trained to minimize the squared distance between predicted and true joint positions. The model is further enhanced by a cascade of DNN-based regressors, where each stage refines the joint predictions using higher resolution sub-images. This cascade structure improves the precision of joint localization. The method is tested on various datasets, including FLIC and LSP, and shows strong performance in challenging scenarios such as occlusions, varying poses, and different lighting conditions. The approach is also evaluated for cross-dataset generalization, demonstrating its effectiveness on related datasets. The results show that the DNN-based approach outperforms other methods, particularly in terms of detection accuracy and localization precision. The method is efficient, with a running time of approximately 0.1 seconds per image, and is capable of achieving high performance with a large number of training examples. The paper concludes that the proposed DNN-based approach is a promising solution for human pose estimation, offering state-of-the-art results on challenging academic datasets.This paper presents a method for human pose estimation using Deep Neural Networks (DNNs). The approach formulates pose estimation as a DNN-based regression problem, where the goal is to predict the coordinates of body joints. A cascade of DNN regressors is used to achieve high precision in joint localization. The method is holistic, meaning it considers the entire body context rather than focusing on individual parts. The DNN-based approach is simple yet powerful, leveraging recent advances in deep learning. The method is evaluated on four academic benchmarks, achieving state-of-the-art or better performance. The key idea is to use a convolutional DNN to regress the positions of body joints based on the full image. This allows the model to capture the full context of each joint, leading to more accurate predictions. The DNN is trained to minimize the squared distance between predicted and true joint positions. The model is further enhanced by a cascade of DNN-based regressors, where each stage refines the joint predictions using higher resolution sub-images. This cascade structure improves the precision of joint localization. The method is tested on various datasets, including FLIC and LSP, and shows strong performance in challenging scenarios such as occlusions, varying poses, and different lighting conditions. The approach is also evaluated for cross-dataset generalization, demonstrating its effectiveness on related datasets. The results show that the DNN-based approach outperforms other methods, particularly in terms of detection accuracy and localization precision. The method is efficient, with a running time of approximately 0.1 seconds per image, and is capable of achieving high performance with a large number of training examples. The paper concludes that the proposed DNN-based approach is a promising solution for human pose estimation, offering state-of-the-art results on challenging academic datasets.