  In this paper, we are presenting a rotation variant Oriented Texture Curve
(OTC) descriptor based mean shift algorithm for tracking an object in an
unstructured crowd scene. The proposed algorithm works by first obtaining the
OTC features for a manually selected object target, then a visual vocabulary is
created by using all the OTC features of the target. The target histogram is
obtained using codebook encoding method which is then used in mean shift
framework to perform similarity search. Results are obtained on different
videos of challenging scenes and the comparison of the proposed approach with
several state-of-the-art approaches are provided. The analysis shows the
advantages and limitations of the proposed approach for tracking an object in
unstructured crowd scenes.
