Learning context-based feature descriptors for object tracking

A major problem with previous object tracking approaches is adapting object representations depending on scene context to account for changes in illumination, viewpoint changes, etc. To adapt our previous approach to deal with background changes, here we first derive some clusters from a training sequence and the corresponding object representations for those clusters. Next, for each frame of a separate test sequence, its nearest background cluster is determined and then the corresponding descriptor of that cluster is used for object representation in this frame. Experiments show that the proposed approach tracks objects and persons in natural scenes more effectively.