Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking

In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and traditional features. Then the results from these two approaches are compared with state-of-the-art trackers. The results are obtained by executing the methods on the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the second method caused up to 15.9%. The proposed methods can still be improved by extracting more distinguishable features

Formation mechanism of ZrB2-Al2O3 nanocomposite powder by mechanically induced self-sustaining reaction

ZrB2-Al2O3 nanocomposite powder was produced by aluminothermic reduction in Al/ZrO2/B2O 3 system. In this research, high energy ball milling was used to produce the necessary conditions to induce a mechanically induced self-sustaining reaction. The ignition time of the composite formation was found to be about 13 min. The synthesis mechanism in this system was investigated by examining the corresponding sub-reactions as well as changing the stoichiometry of reactants. Thermal behavior of the system was also studied. © 2013 Springer Science+Business Media New York.Peer Reviewe