Human Body Pose Estimation with Particle Swarm Optimisation

2008 ◽  
Vol 16 (4) ◽  
pp. 509-528 ◽  
Author(s):  
Špela Ivekovič ◽  
Emanuele Trucco ◽  
Yvan R. Petillot
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
Particle Swarm ◽  
Search Space ◽  
The Body ◽  
Particle Swarm Optimisation ◽  
Human Body Model ◽  
Still Images ◽  
Body Pose Estimation

In this paper we address the problem of human body pose estimation from still images. A multi-view set of images of a person sitting at a table is acquired and the pose estimated. Reliable and efficient pose estimation from still images represents an important part of more complex algorithms, such as tracking human body pose in a video sequence, where it can be used to automatically initialise the tracker on the first frame. The quality of the initialisation influences the performance of the tracker in the subsequent frames. We formulate the body pose estimation as an analysis-by-synthesis optimisation algorithm, where a generic 3D human body model is used to illustrate the pose and the silhouettes extracted from the images are used as constraints. A simple test with gradient descent optimisation run from randomly selected initial positions in the search space shows that a more powerful optimisation method is required. We investigate the suitability of the Particle Swarm Optimisation (PSO) for solving this problem and compare its performance with an equivalent algorithm using Simulated Annealing (SA). Our tests show that the PSO outperforms the SA in terms of accuracy and consistency of the results, as well as speed of convergence.

Multi-View Human Body Pose Estimation with CUDA-PSO

2012 ◽  
Vol 3 (4) ◽  
pp. 51-65 ◽  
Author(s):  
Luca Mussi ◽  
Spela Ivekovic ◽  
Youssef S.G. Nashed ◽  
Stefano Cagnoni
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
Optimization Problem ◽  
Graphics Processing Unit ◽  
Fitness Function ◽  
Optimization Method ◽  
The Body ◽  
Processing Unit ◽  
Graphics Processing ◽  
Body Pose Estimation

The authors formulate the body pose estimation as a multi-dimensional nonlinear optimization problem, suitable to be approximately solved by a meta-heuristic, specifically, the particle swarm optimization (PSO). Starting from multi-view video sequences acquired in a studio environment, a full skeletal configuration of the human body is retrieved. They use a generic subdivision-surface body model in 3-D to generate solutions for the optimization problem. PSO then looks for the best match between the silhouettes generated by the projection of the model in a candidate pose and the silhouettes extracted from the original video sequence. The optimization method, in this case PSO, is run in parallel on the Graphics Processing Unit (GPU) and is implemented in Cuda-C™ on the nVidia CUDA™ architecture. The authors compare the results obtained by different configurations of the camera setup, fitness function, and PSO neighborhood topologies.

A New Method to Concentrate Electromagnetic Field Within ROI Using a High Dielectric Material in 3T Body MRI

2013 ◽  
Author(s):  
Bu S. Park ◽  
Sunder S. Rajan ◽  
Leonardo M. Angelone
Keyword(s):  
Numerical Simulation ◽  
Electromagnetic Field ◽  
Human Body ◽  
Region Of Interest ◽  
Dielectric Materials ◽  
The Body ◽  
Human Body Model ◽  
Body Model ◽  
Simulation Results ◽  
High Dielectric

We present numerical simulation results showing that high dielectric materials (HDMs) when placed between the human body model and the body coil significantly alter the electromagnetic field inside the body. The numerical simulation results show that the electromagnetic field (E, B, and SAR) within a region of interest (ROI) is concentrated (increased). In addition, the average electromagnetic fields decreased significantly outside the region of interest. The calculation results using a human body model and HDM of Barium Strontium Titanate (BST) show that the mean local SAR was decreased by about 56% (i.e., 18.7 vs. 8.2 W/kg) within the body model.

scholarly journals Cognitive Bare Bones Particle Swarm Optimisation with Jumps

2016 ◽  
Vol 7 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Mohammad Majid al-Rifaie ◽  
Tim Blackwell
Keyword(s):  
Probability Distribution ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Swarm Optimisation ◽  
Early Stages ◽  
Bare Bones

The ‘bare bones' (BB) formulation of particle swarm optimisation (PSO) was originally advanced as a model of PSO dynamics. The idea was to model the forces between particles with sampling from a probability distribution in the hope of understanding swarm behaviour with a conceptually simpler particle update rule. ‘Bare bones with jumps' (BBJ) proposes three significant extensions to the BB algorithm: (i) two social neighbourhoods, (ii) a tuneable parameter that can advantageously bring the swarm to the ‘edge of collapse' and (iii) a component-by-component probabilistic jump to anywhere in the search space. The purpose of this paper is to investigate the role of jumping within a specific BBJ algorithm, cognitive BBJ (cBBJ). After confirming the effectiveness of cBBJ, this paper finds that: jumping in one component only is optimal over the 30 dimensional benchmarks of this study; that a small per particle jump probability of 1/30 works well for these benchmarks; jumps are chiefly beneficial during the early stages of optimisation and finally this work supplies evidence that jumping provides escape from regions surrounding sub-optimal minima.

The Key Role of Headrest Optimization in Driver Comfort

2019 ◽  
Vol 24 (3) ◽  
pp. 592-599
Author(s):  
Hamid Gheibollahi ◽  
Masoud Masih-Tehrani ◽  
Mohammadmehdi Niroobakhsh
Keyword(s):  
Human Body ◽  
The Body ◽  
Human Body Model ◽  
Sitting Posture ◽  
Conventional Vehicle ◽  
Body Model ◽  
Basic Model ◽  
Proposed Model ◽  
Stiffness And Damping

In this study, adding a headrest to the conventional vehicle driver seat is investigated to improve the driver comfort and decrease the driver damages. For this purpose, a conventional biomechanical human body model of wholebody vibrations is provided and modified by adding a head degree of freedom to the body model and a headrest to the seat model. The basic model is in the sitting posture, lumped parameters and has nine DOFs for the human body, on contrary to the proposed model which has ten DOFs. The new human body DOF is the twisting motion of the head and neck. This new DOF is generated because of headrest adding to the driver’s seat. To determine the head discomforts, the Seat to Head (STH) indexes are studied in two directions: horizontal and vertical. The Genetic Algorithm (GA) is used to optimize the STH in different directions. The optimization variables are stiffness and damping parameters of the driver’s seat which are 12 for the basic model and are 16 for a new seat. The integer programming is used for time reduction. The results show that new seat (equipped by headrest) has very better STH in both directions.

scholarly journals Pengaruh Kebersihan Tangan Pada Saat Makan Menggunakan Terhadap Kontaminasi Pada Makanan

2019 ◽  
Author(s):  
Amira Dina Luthfiyyani
Keyword(s):  
Human Body ◽  
Research Method ◽  
Food Contamination ◽  
The Body ◽  
Literature Study ◽  
Body Interaction ◽  
Quality Of Food ◽  
Food Consumed

Abstract, There are several ways used by others to eat their food, one of which is using their own hands without cutlery. Eating with hands more people use this to make food tastier with the practicality of eating using hands is also one of the reasons many people like to use their hands as cutlery. The writing of this paper aims to find out how to eat by hand and cleanliness of food contamination and the quality of food consumed. The research method used was a literature study by analyzing the contents of eating using hands, body interaction, and the influence of eating using hands to understand the body. The results of this study indicate that eating using hands can affect the health of the human body.

Sign in / Sign up

Export Citation Format

Share Document