Online object tracking: A benchmark

Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on shar-ing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evalua-tion criteria to understand how these algorithms perform. The test image sequences are annotated with different at-tributes for performance evaluation and analysis. By ana-lyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research di-rections in this field. 1