In recent years, IT convergence technology has been developed to get creative solution by combining robotics or sports science technology. Object detection and recognition have mainly applied to sports science field that has processed by recognizing face and by tracking human body. But object detection and recognition using vision sensor is challenge task in real world because of illumination. ...

In recent years, convolutional neural networks (CNNs) have been applied successfully to recognise persons, their body parts and pose keypoints in photos videos. The transfer of these techniques artificially created images is rather unexplored, though challenging since are drawn different styles, proportions, levels abstraction. this work, we study problems on the basis pictorial maps where iden...

We address the problem of accurately estimating human shape, pose, and motion from images and video without markers or special cameras. Existing methods often assume known backgrounds, static cameras, and sequence specific motion priors. Here we propose a method that is fully automatic and, given multi-view video, estimates 3D human motion and body shape. Our work is built upon the recent SMPLi...

This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast to other popular methods in the current literature that train separate classifiers for poses and individual attributes, our method explicitly represents the decomposition and a...

This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast to other popular methods in the current literature that train separate classifiers for poses and individual attributes, our method explicitly represents the decomposition and a...

This paper deals with the problem of estimating the effort required to maintain a static pose by human beings. The problem is important in developing dance summarization and rehabilitation applications. We estimate the human pose effort using two kinds of body constraints – skeletal constraints and gravitational constraints. The extracted features are combined together using SVM regression to e...

This paper addresses the problem of accurate and robust tracking of 3D human body pose from depth image sequences. Recovering the large number of degrees of freedom in human body movements from a depth image sequence is challenging due to the need to resolve the depth ambiguity caused by self-occlusions and the difficulty to recover from tracking failure. Human body poses could be estimated thr...

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset [1] with additional motion features. We apply our architecture to this dataset and report significantly better perf...