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

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available