Discriminative Scale Space Tracking

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our…

• E
  EMC
• N
  Nvidia
• FF
  Fellowships Fund Incorporated
• SF
  Stiftelsen för Strategisk Forskning
• CU
  Chongqing University of Arts and Sciences
• V
  Vetenskapsrådet