The goal of this thesis is to design methods to estimate the local concentration and velocity of particles observed in digital videos of the inner wall of a circulating fluidized bed (CFB) riser. Understanding the dynamics of these rapidly moving particles will allow researchers to design cleaner and more efficient CFB facilities. However, the seemingly random motion exhibited by the particles in three dimensions, coupled with the varying image quality, make it difficult to extract the required information from the images. Given a video sequence, a method for detecting particles and tracking their spatial location is developed. By exploiting the presence of specular reflections, individual particles are first identified along the focal plane by an image filter specifically designed for this purpose. Once the particle locations are known, a local optical flow model is used to approximate the motion field across two images in order to track particles from one frame of the sequence to another. An evaluation of the proposed method indicates its potential to estimate particle count, location, concentration and velocity in an efficient and reliable manner. The method is fully automated and is expected to be an important analysis tool for researchers with the National Energy Technology Laboratory, part of the national laboratory system of the Department of Energy.