On deep learning in physics

Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of training. This learning-by-example approach has slowly made its way into the physical sciences in recent years. In this dissertation I present a collection of contributions at the intersection of the fields of physics and deep learning. These contributions constitute some of the earlier introductions of deep learning to the physical sciences, and comprises a range of machine learning techniques, such as feed forward neural networks, generative models, and reinforcement learning. A focus will be placed on the lessons and techniques learned along the way that would influence future research projects

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application