Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario

Impacts of local human activities on the Antarctic environment

We review the scientific literature, especially from the past decade, on the impacts of human activities on the Antarctic environment. A range of impacts has been identified at a variety of spatial and temporal scales. Chemical contamination and sewage disposal on the continent have been found to be long-lived. Contemporary sewage management practices at many coastal stations are insufficient to prevent local contamination but no introduction of non-indigenous organisms through this route has yet been demonstrated. Human activities, particularly construction and transport, have led to disturbances of flora and fauna. A small number of non-indigenous plant and animal species has become established, mostly on the northern Antarctic Peninsula and southern archipelagos of the Scotia Arc. There is little indication of recovery of overexploited fish stocks, and ramifications of fishing activity oil bycatch species and the ecosystem could also be far-reaching. The Antarctic Treaty System and its instruments, in particular the Convention for the Conservation of Antarctic Marine Living Resources and the Environmental Protocol, provide a framework within which management of human activities take place. In the face of the continuing expansion of human activities in Antarctica, a more effective implementation of a wide range of measures is essential, in order to ensure comprehensive protection of the Antarctic environment, including its intrinsic, wilderness and scientific values which remains a fundamental principle of the Antarctic Treaty System. These measures include effective environmental impact assessments, long-term monitoring, mitigation measures for non-indigenous species, ecosystem-based management of living resources, and increased regulation of National Antarctic Programmes and tourism activities

2010 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

Abstract book for the Second Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: November 19 - 20, 2010Plenary speaker: Abdul-Aziz Yakubu, Professor and Chair of the Department of Mathematics, Howard UniversityFeatured speaker: Jory Weintraub, Assistant Director Education and Outreach, National Evolutionary Synthesis Cente

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Systematic Parameter Optimization and Application of Automated Tracking in Pedestrian-Dominant Situations

RÉSUMÉ Les mouvements des piétons et leur modélisation constituent un domaine de recherche de plus en plus actif. Bien qu’encore souvent appliqué à la sécurité par l’élaboration de plans d’évacuation en cas d’urgence, comprendre le mouvement des piétons est un enjeu économique de plus en plus important, notamment pour améliorer l’efficacité des aménagements de transport et des grands centres commerciaux. Cependant, les données existantes — particulièrement au niveau individuel, ou microscopique —sont majoritairement collectées dans des situations expérimentales contrôlées. Elles ne sont donc pas nécessairement représentatives du comportement des piétons dans des situations réelles, particulièrement en tenant compte de la susceptibilité de leur comportement aux facteurs démographiques, psychologiques et nvironnementaux. Cette lacune est due principalement à l’absence de méthodes prouvées pour la détection et le suivi de piétons dans des cas réels, absence qui résulte de la complexité des mouvements piétons et qui persiste malgré l’avancement continu des méthodes automatique d’analyse.----------ABSTRACT Though a wealth of data exists for the characterization of pedestrian movement, a majority of it originates from experimental settings owing to the current state of trackers for real-world scenarios. While these trackers are steadily improving, they remain insufficiently reliable for the accurate, microscopic tracking of individuals, particularly in cases of occlusion or higher density, complex scenes. In this work, the use of evolution algorithms is proposed for the systematic calibration of the parameters of existing trackers in order to further optimize their performance – evaluated by tracking accuracy and precision metrics – in complex cases, with an initial focus on two tracking methods designed for multimodal analysis. This calibration is further aided by the inclusion of additional parameters regulating homography, or specifically the plane to which tracker detections are projected. Three real test cases were used: a) a confined corridor in a public building, b) a subway station entrance during morning rush hour and c) a crosswalk in downtown New York. Results demonstrate a halving of tracking errors over both default and manually-calibrated parameters, as well as a strong correlation in performance between similar cases. These results were consistent over multiple trials and regardless of the starting parameters, strongly implying that the obtained solutions are indeed the global maxima for each scene. For application and validation of the resultant tracks, flow characterization and directional counting are demonstrated, utilizing tools included in the optimization framework

Collective Behaviour: From Cells to Humans

Living in organised groups is a strategy that can be observed in a multitude of diverse species. Among such species, the behaviour of an individual on their own is not the same as within a group: the environment is modified by the presence of more subjects, individuals interact with each other, and from those interactions complex patterns of behaviour can emerge. Some species of animals almost exclusively exist as groups, and as a consequence, studying them in a social context is the only way to understand their behaviour in nature. This is the idea that drives all the research presented in this thesis: the particular behaviour exhibited by the group is so robust that it will emerge even in a very simplified environment. By observing the individual and the group in those simplified experimental conditions, it is possible to deduce rules that might govern the interaction. The importance of interactions in the group’s behaviour can then be demonstrated by implementing a computer model of agents following those rules and comparing it with natural and experimental behaviour. This thesis presents different examples of such analyses, and gives illustrations of the range of questions that can be answered through this method. Groups of stem cells, juvenile sea bass and human beings were successively observed and tracked in suitable environments, with or without perturbation. The data extracted from those experiments were then processed so as to correct recording errors, and individual and collective behaviours were derived from those data, returning new insights on the nature of the interaction at the individual level, their consequences at the global level, as well as the effects of the interaction on both. Finally, I present the computer models derived from those analyses. Many systems in nature share this property of global behaviours emerging from deterministic local interaction, and as a consequence studies of this kind could shed light on important questions, of which cancer treatment, ocean acidification and human organisations are but a few examples