Video Object Avoidance Implementation on Embedded Platform

Motion detection is fundamental in various computer vision related applications. In this project, there are two motion detection techniques being studied, namely optical flow and motion templates. This is to detect the moving obstacles as well as to classify the direction of the moving obstacles. Optical flow is the computation to approximate the image motion, while motion templates use the motionhistory- image (MHI) to keep track of the most recent movement with the timestamp. Besides, this project also covers the static object detection, where HSV color model classification technique is used to detect the static obstacles. This technique is based on filtration of color, which depending on the HSV values of the static objects. Both motion and static detection algorithms will be tested in Window Visual Studio 2010, before implementing them into the embedded platform, which is Raspberry Pi. Meanwhile, OpenCV is used as the computer vision library throughout the project. At the end of this project object, motion templates is selected as a more suitable motion detection techniques due to its extra information, which is the angle. The HSV technique can detect the static objects but limited to the calibrated color onl

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity