*Articulated Human Pose Estimation Using Greedy Approach DOI: http://dx.doi.org/10.5772/intechopen.99354*

feature, three-stage deep CNN was adopted to maintain temporal consistency of video by halfway temporal evaluation method and structured space learning. Speeded up Robust features (SURF) and Scale Invariant Feature Transform (SIFT) was proposed by A. Agarwal, D. Samaiya and K. K. Gupta to deal with blur and illumination changes for different background conditions [18]. Paper [19] aims to improve human ergonomics using Wireless vibrotactile displays in the execution of repetitive or heavy industrial tasks. Different approach was presented to detect human pose. Coarse-Fine Network for Key point Localization (CFN)[20], G-RMI [21] and Regional Multi-person Pose Estimation (RMPE)[22] techniques have been used to implement top-down approach of pose detection (i.e. the person is identified first and then the body parts). An alternate bottom-up approach was proposed by Z. Cao, T. Simon, S. Wei and Y. Sheikh based on Partial Affinity Fields to efficiently detect the 2D pose [23]. X. Chen and G. Yang also presented a generic multi-person bottom-up approach for pose estimation formulated as a set of bipartite graph matching by introducing limb detection heatmaps. These heatmaps represent association of body joint pairs, that are simultaneously learned with joint detection [24]. L. Ke, H. Qi, M. Chang and S. Lyu proposed a deep conv-deconv modules-based pose estimation method via keypoint association using a regression network [25]. K. Akila and S. Chitrakala introduced a highly discriminating HOI descriptor to recognize human action in a video. The focus is to discriminate identical spatio-temporal relations actions by human-object interaction analysis and with similar motion pattern [26] Y. Yang and D. Ramanan proposed methods for pose detection and estimation for static images based on deformable part models with augmentation of standard pictorial structure model by co-occurrence relations between spatial relations of part location and part mixtures [27]. A Three-dimensional (3D) human pose estimation methods are explored and reviewed in a paper, it nvolves estimating the articulated 3D joint locations of a human body from an image or video [28]. One more study includes a 2-D technique which localize dense landmark on the entire body like face, hands and even on skin [29].
