scholarly journals A Bayesian Framework for Human Body Pose Tracking from Depth Image Sequences

Sensors ◽  
2010 ◽  
Vol 10 (5) ◽  
pp. 5280-5293 ◽  
Author(s):  
Youding Zhu ◽  
Kikuo Fujimura
Keyword(s):  
Human Body ◽  
Bayesian Framework ◽  
Image Sequences ◽  
Depth Image ◽  
Pose Tracking

The measurement method of human body bust based on Kinect depth image

2015 ◽  
pp. 231-235
Author(s):  
Y Chong ◽  
Y Wang ◽  
S Pan ◽  
Z Wang ◽  
H Dai ◽  
...  
Keyword(s):  
Human Body ◽  
Measurement Method ◽  
Depth Image

scholarly journals Graph Cut-Based Human Body Segmentation in Color Images Using Skeleton Information from the Depth Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 393 ◽  
Author(s):  
Jonha Lee ◽  
Dong-Wook Kim ◽  
Chee Won ◽  
Seung-Won Jung
Keyword(s):  
Human Body ◽  
Color Image ◽  
Depth Image ◽  
Body Region ◽  
Reasonable Accuracy ◽  
Depth Sensor ◽  
Complicated Shape ◽  
Depth Sensors ◽  
Body Segmentation ◽  
Human Body Segmentation

Segmentation of human bodies in images is useful for a variety of applications, including background substitution, human activity recognition, security, and video surveillance applications. However, human body segmentation has been a challenging problem, due to the complicated shape and motion of a non-rigid human body. Meanwhile, depth sensors with advanced pattern recognition algorithms provide human body skeletons in real time with reasonable accuracy. In this study, we propose an algorithm that projects the human body skeleton from a depth image to a color image, where the human body region is segmented in the color image by using the projected skeleton as a segmentation cue. Experimental results using the Kinect sensor demonstrate that the proposed method provides high quality segmentation results and outperforms the conventional methods.

scholarly journals Computer Vision Intelligent Approaches to Extract Human Pose and Its Activity from Image Sequences

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 159
Author(s):  
Paulo J. S. Gonçalves ◽  
Bernardo Lourenço ◽  
Samuel Santos ◽  
Rodolphe Barlogis ◽  
Alexandre Misson
Keyword(s):  
Computer Vision ◽  
Human Body ◽  
Prediction Models ◽  
Image Sequences ◽  
Video Frame ◽  
Support Vector ◽  
Training Phase ◽  
Human Body Model ◽  
Body Model ◽  
Human Pose

The purpose of this work is to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), support vector machines (SVM) and long short-term memory networks (LSTM) to predict human pose and activity from image sequences, based on computer vision approaches to gather the required features. To obtain the human pose semantics (output classes), based on a set of 3D points that describe the human body model (the input variables of the predictive model), prediction models were obtained from the acquired data, for example, video images. In the same way, to predict the semantics of the atomic activities that compose an activity, based again in the human body model extracted at each video frame, prediction models were learned using LSTM networks. In both cases the best learned models were implemented in an application to test the systems. The SVM model obtained 95.97% of correct classification of the six different human poses tackled in this work, during tests in different situations from the training phase. The implemented LSTM learned model achieved an overall accuracy of 88%, during tests in different situations from the training phase. These results demonstrate the validity of both approaches to predict human pose and activity from image sequences. Moreover, the system is capable of obtaining the atomic activities and quantifying the time interval in which each activity takes place.

Training a CNN to robustly segment the human body parts in range image sequences

2019 ◽  
Author(s):  
Lama Seoud ◽  
Jonathan Boisvert ◽  
Marc-Antoine Drouin ◽  
Michel Picard ◽  
Guy Godin
Keyword(s):  
Human Body ◽  
Image Sequences ◽  
Range Image ◽  
Body Parts
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