Metaheuristic Optimization Techniques for Articulated Human Tracking

Four adaptive metaheuristic optimization algorithms are proposed and demonstrated: Adaptive Parameter Particle Swarm Optimization (AP-PSO), Modified Artificial Bat (MAB), Differential Mutated Artificial Immune System (DM-AIS) and hybrid Particle Swarm Accelerated Artificial Immune System (PSO-AIS). The algorithms adapt their search parameters on the basis of the fitness of obtained solutions such that a good fitness value favors local search, while a poor fitness value favors global search. This efficient feedback of the solution quality, imparts excellent global and local search characteristic to the proposed algorithms. The algorithms are tested on the challenging Articulated Human Tracking (AHT) problem whose objective is to infer human pose, expressed in terms of joint angles, from a continuous video stream. The Particle Filter (PF) algorithms, widely applied in generative model based AHT, suffer from the 'curse of dimensionality' and 'degeneracy' challenges. The four proposed algorithms show stable performance throughout the course of numerical experiments. DM-AIS performs best among the proposed algorithms followed in order by PSO-AIS, AP-PSO, and MBA in terms of Most Appropriate Pose (MAP) tracking error. The MAP tracking error of the proposed algorithms is compared with four heuristic approaches: generic PF, Annealed Particle Filter (APF), Partitioned Sampled Annealed Particle Filter (PSAPF) and Hierarchical Particle Swarm Optimization (HPSO). They are found to outperform generic PF with a confidence level of 95%, PSAPF and HPSO with a confidence level of 85%. While DM-AIS and PSO-AIS outperform APF with a confidence level of 80%. Further, it is noted that the proposed algorithms outperform PSAPF and HPSO using a significantly lower number of function evaluations, 2500 versus 7200. The proposed algorithms demonstrate reduced particle requirements, hence improving computational efficiency and helping to alleviate the 'curse of dimensionality'. The adaptive nature of the algorithms is found to guide the whole swarm towards the optimal solution by sharing information and exploring a wider solution space which resolves the 'degeneracy' challenge. Furthermore, the decentralized structure of the algorithms renders them insensitive to accumulation of error and allows them to recover from catastrophic failures due to loss of image data, sudden change in motion pattern or discrete instances of algorithmic failure. The performance enhancements demonstrated by the proposed algorithms, attributed to their balanced local and global search capabilities, makes real-time AHT applications feasible. Finally, the utility of the proposed algorithms in low-dimensional system identification problems as well as high-dimensional AHT problems demonstrates their applicability in various problem domains

Modelling and controlling of integrated photovoltaic-module and converter systems for partial shading operation using artificial intelligence

The thesis has three main themes: analysis and optimal design of Cuk DC-DC converters; integration of Cuk DC-DC converters with photovoltaic (PV) modules to improve operation during partial shading; and an artificial intelligence model for the PV module, permitting an accurate maximum power point (MPP) tracking in the integrated system. The major contribution of the thesis is the control of an integrated photovoltaic module and DC-DC converter configuration for obtaining maximum power generation under non-uniform solar illumination. In place of bypass diodes, the proposed scheme embeds bidirectional Cuk DC-DC converters within the serially connected PV modules. A novel control scheme for the converters has been developed to adjust their duty ratios, enabling all the PV modules to operate at the MPPs corresponding to individual lighting conditions. A detailed analysis of a step-down Cuk converter has been carried out leading to four transfer functions of the converter in two modes, namely variable input - constant output voltage, and variable output - constant input voltage. The response to switch duty ratio variation is shown to exhibit a non-minimum phase feature. A novel scheme for selecting the circuit components is developed using the criteria of suppressing input current and output voltage ripple percentages at a steady state, and minimising the time integral of squared transient response errors. The designed converter has been tested in simulation and in practice, and has been shown to exhibit improved responses in both operating modes. A Neuro-Fuzzy network has been applied in modelling the characteristics of a PV module. Particle-Swarm-Optimisation (PSO) has been employed for the first time as the training algorithm, with which the tuning speed has been improved. The resulting model has optimum compactness and interpretability and can predict the MPPs of individual PV modules in real time. Experimental data have confirmed its improved accuracy. The tuned Neuro-Fuzzy model has been applied to a practical PV power generation system for MPP control. The results have shown an average error of 1.35% compared with the maximum extractable power of the panel used. The errors obtained, on average, are also about four times less than those using the genetic-algorithm-based model proposed in a previous research. All the techniques have been incorporated in a complete simulation system consisting of three PV panels, one boost and two bidirectional Cuk DC-DC converters. This has been compared under the same weather conditions as the conventional approach using bypass diodes. The results have shown that the new system can generate 32% more power

Articulated human motion tracking with HPSO

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