A general object tracker for locating objects in digital video

Abstract

Tracking a human head in a complicated scene with changing object pose, illumination conditions, and many occluding objects, is the subject of this thesis. A general tracking algorithm is presented, which uses a combination of object color statistics and texture features with motion estimation. The object is defined by an ellipse window that is initially selected by the user. Color statistics are obtained by calculating object color histogram in the YCrCb space, with more resolution reserved for chroma components. In addition to the conventional discrete color histogram, a novel method, Uniform Fuzzy Color Histogram (UFCH) is proposed. The object texture is represented by lower frequency components of the object's discrete cosine transform (DCT), and local binary patterns (LBP). By using the tracker, performances of different features and their combinations are tested. The tracking procedure is based on constant velocity motion estimation by condensation particle filter, in which the sample set is obtained by the translation of the object window. Histogram comparison is based on Bhattacharyya coefficient, and DCT comparison is calculated by sum of squared differences (SSD). Similarity measures are joined by combining their independent likelihoods. As the combined tracker follows different features of the same object, it is an improvement over a tracker that makes use of only color statistics or texture information. The algorithm is tested and optimized on the specific application of embedding interactive object information to movies.