A Study on Human Motion Acquisition and Recognition Employing Structured Motion Database

九州工業大学博士学位論文 学位記番号:工博甲第332号　学位授与年月日:平成24年3月23日1 Introduction||2 Human Motion Representation||3 Human Motion Recognition||4 Automatic Human Motion Acquisition||5 Human Motion Recognition Employing Structured Motion Database||6 Analysis on the Constraints in Human Motion Recognition||7 Multiple Persons’ Action Recognition||8 Discussion and ConclusionsHuman motion analysis is an emerging research field for the video-based applications capable of acquiring and recognizing human motions or actions. The automaticity of such a system with these capabilities has vital importance in real-life scenarios. With the increasing number of applications, the demand for a human motion acquisition system is gaining importance day-by-day. We develop such kind of acquisition system based on body-parts modeling strategy. The system is able to acquire the motion by positioning body joints and interpreting those joints by the inter-parts inclination. Besides the development of the acquisition system, there is increasing need for a reliable human motion recognition system in recent years. There are a number of researches on motion recognition is performed in last two decades. At the same time, an enormous amount of bulk motion datasets are becoming available. Therefore, it becomes an indispensable task to develop a motion database that can deal with large variability of motions efficiently. We have developed such a system based on the structured motion database concept. In order to gain a perspective on this issue, we have analyzed various aspects of the motion database with a view to establishing a standard recognition scheme. The conventional structured database is subjected to improvement by considering three aspects: directional organization, nearest neighbor searching problem resolution, and prior direction estimation. In order to investigate and analyze comprehensively the effect of those aspects on motion recognition, we have adopted two forms of motion representation, eigenspace-based motion compression, and B-Tree structured database. Moreover, we have also analyzed the two important constraints in motion recognition: missing information and clutter outdoor motions. Two separate systems based on these constraints are also developed that shows the suitable adoption of the constraints. However, several people occupy a scene in practical cases. We have proposed a detection-tracking-recognition integrated action recognition system to deal with multiple people case. The system shows decent performance in outdoor scenarios. The experimental results empirically illustrate the suitability and compatibility of various factors of the motion recognition

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

A comparison of the Kalman filter and recurrent neural networks for state estimation of dynamical systems

The study of dynamical systems is of great interest in many fields, with a wide range of applications. In some cases, these dynamical systems may be affected by noise and the availability of measurements may be limited. State estimations methods which can account for these challenges are valuable tools in analyzing these systems. While for linear systems the standard method is by using an algorithm called the Kalman filter, data-driven methods employing the versatility of artificial neural networks have also been proposed. In this thesis, we first introduce state estimation using the Kalman filter. Next, we provide an overview of a type of artificial neural network called recurrent neural networks (RNNs), which are particularly suited for tasks on time series data. We finally present the results of implementing RNN-based estimators for a number of dynamical systems with comparisons to Kalman filtering

Експериментальна економіка та машинне навчання для прогнозування динаміки емерджентної економіки: матеріали вибраних робіт 8-ї Міжнародної конференції з моніторингу, моделювання та управління емерджентною економікою (M3E2 2019)

This volume represents the proceedings of the selected papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019), held in Odessa, Ukraine, on May 22-24, 2019. It comprises 38 papers dedicated to the experimental economics and machine learning that were carefully peer-reviewed and selected from 71 submissions.Цей том представляє вибрані матеріали 8-ої Міжнародної конференції "Моніторинг, моделювання та менеджмент емерджентної економіки" (M3E2 2019), що відбулася в Одесі, Україна, 22-24 травня 2019 року. Він містить 38 робіт, присвячених експериментальній економіці та машинному навчанню, які були ретельно прорецензовані та відібрані з 71 подання

8 Міжнародна конференція "Моніторинг, моделювання та менеджмент емерджентної економіки" (M3E2 2019)

Матеріали конференції.Proceedings