Object recognition in complex video scenes for advertising applications

Traditional image classifiers suffer from the so called "Background clutter problem", where they have to, simultaneously, localize and recognize an object of interest. In real-world video scenes, many objects appear at the same time and several objects of interest could be present. We decompose the problem into two stages. The first involves extracting a moving object from the video scene based on its coherent motion. This step makes no assumption about the nature of the object being tracked and extracted. We, then apply a classifier to the extracted object to determine if it falls into one of the classes of interest.