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Human Body Pose Estimation from Multi-View Video

Human Body Pose Estimation with PSO
S. Ivekovic and E. Trucco


In this paper we describe the application of Particle Swarm Optimisation to the problem of human body pose estimation from multiple view video sequences. We use a subdivision body model with an underlying skeleton layer to estimate and illustrate the body pose. The optimisation looks for the best match between the silhouettes extracted from the original video sequence and the silhouettes generated by the projection of the model in a pose suggested by the PSO. The original PSO algorithm is applied hierarchically and combined with the full overall optimisation to decrease the effects of error propagation. Results demonstrate the ability of PSO to reliably recover the correct body pose from 4-viewpoint video sequences.
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Human Body Pose EstimationWith Particle Swarm Optimisation
S. Ivekovic, E. Trucco and Y. R. Petillot



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 3-D 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.
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Articulated Human Tracking Using HPSO
V. John, S. Ivekovic and E. Trucco


In this paper, we address full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional nonlinear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult nonlinear optimisation problems. Our tracking approach is
designed to address the limits of particle filtering approaches: it initialises automatically, removes the need for a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We quantitatively compare the performance of HPSO with that of the particle filter (PF) and annealed particle filter (APF). Our test results, obtained using the framework proposed by (Balan et al., 2005) to compare articulated body tracking algorithms, show that HPSO’s pose estimation accuracy and consistency is better than PF and compares favourably with the APF, outperforming it in sequences with sudden and fast motion.
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Articulated Human Tracking Using Hierarchical Particle Swarm Optimisation
V. John, E. Trucco and S. Ivekovic


In this paper, we address markerless full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional non-linear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult non-linear optimisation problems. We show that a small number of particles achieves accuracy levels comparable with several recent algorithms. PSO initialises automatically, does not need a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter (PF), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF) using the computational framework provided by Balan
et al. HPSO accuracy and consistency are better than PF and compare favourably with those of APF and PSAPF, outperforming it in sequences with sudden and fast motion. We also report an extensive experimental study of HPSO over ranges of values of its parameters.
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Markerless Multi-View Articulated Pose Estimation Using Adaptive Hierarchical Particle Swarm Optimisation
S. Ivekovic, V. John and E. Trucco


In this paper, we present a new adaptive approach to multi-view markerless articulated human body pose estimation from multi-view video sequences, using Particle Swarm Optimisation (PSO). We address the computational complexity of the recently developed hierarchical PSO (HPSO) approach, which successfully estimated a wide range of different motion with a fixed set of parameters, but incurred an unnecessary overhead in computational complexity. Our adaptive approach, called APSO, preserves the black-box property of the HPSO in that it requires no parameter value input from the user. Instead, it adaptively changes the value of the search parameters online, depending on the quality of the pose estimate in the preceding frame of the sequence. We experimentally compare our adaptive approach with HPSO on four different video sequences and show that the computational complexity can be reduced without sacrificing accuracy and without requiring any user input or prior knowledge about the estimated motion type.
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Markerless Articulated Human Body Tracking from Multi-View Video with GPU-PSO
L. Mussi, S. Ivekovic and S. Cagnoni


In this paper, we describe the GPU implementation of a markerless full-body articulated human motion tracking system from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multidimensional nonlinear optimisation problem solved using particle swarm optimisation (PSO).We model the human body pose with a skeleton-driven subdivisionsurface human body model. The optimisation 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. In formulating the solution, we exploit the inherent parallel nature of PSO to formulate a GPUPSO, implemented within the nVIDIA CUDA architecture. Results demonstrate that the GPU-PSO implementation recovers the articulated body pose from 10-viewpoint video sequences with significant computational savings when compared to the sequential implementation, thereby increasing the practical potential of our markerless pose estimation approach.
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