scholarly journals Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Yi Li ◽  
Zhengxing Sun
Keyword(s):  
Genetic Algorithm ◽  
Prior Knowledge ◽  
Motion Tracking ◽  
Estimation Method ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Immune Genetic Algorithm ◽  
Human Motion Tracking ◽  
Latent Space ◽  
Pose Optimization

We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA) for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA) algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.

scholarly journals Towards Human Motion Tracking Enhanced by Semi-Continuous Ultrasonic Time-of-Flight Measurements

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2259
Author(s):  
Silje Ekroll Jahren ◽  
Niels Aakvaag ◽  
Frode Strisland ◽  
Andreas Vogl ◽  
Alessandro Liberale ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Time Of Flight ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Distributed Sensors ◽  
Human Motion Tracking ◽  
Ultrasonic Time ◽  
Flight Measurements

Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson’s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab-settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of-flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking.

scholarly journals Markerless Multi-view Human Motion Tracking Using Manifold Model Learning by Charting

2012 ◽  
Vol 41 ◽  
pp. 664-670 ◽  
Author(s):  
Sanjay Saini ◽  
Dayang Rohaya Bt Awang Rambli ◽  
Suziah Bt Sulaiman ◽  
M Nordin B Zakaria ◽  
Siti Rohkmah
Keyword(s):  
Motion Tracking ◽  
Human Motion ◽  
Model Learning ◽  
Human Motion Tracking

scholarly journals 3D Human Motion Tracking and Reconstruction Using DCT Matrix Descriptor

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Alireza Behrad ◽  
Nadia Roodsarabi
Keyword(s):  
3D Reconstruction ◽  
Discrete Cosine Transform ◽  
Motion Tracking ◽  
Image Sequence ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Image Descriptor ◽  
Tracking Process ◽  
Position And Orientation ◽  
Proper Frequency

One of the most important issues in human motion analysis is the tracking and 3D reconstruction of human motion, which utilizes the anatomic points' positions. These points can uniquely define the position and orientation of all anatomical segments. In this work, a new method is proposed for tracking and 3D reconstruction of human motion from the image sequence of a monocular static camera. In this method, 2D tracking is used for 3D reconstruction, which a database of selected frames is used for the correction of tracking process. The method utilizes a new image descriptor based on discrete cosine transform (DCT), which is employed in different stages of the algorithm. The advantage of using this descriptor is the capabilities of selecting proper frequency regions in various tasks, which results in an efficient tracking and pose matching algorithms. The tracking and matching algorithms are based on reference descriptor matrixes (RDMs), which are updated after each stage based on the frequency regions in DCT blocks. Finally, 3D reconstruction is performed using Taylor’s method. Experimental results show the promise of the algorithm.

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