Fast Compressive Tracking

It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance…

• NS
  National Science Foundation

  Awards: 1149783, 1152576
• NN
  National Natural Science Foundation of China
• HK
  Hong Kong Polytechnic University

  Award: G-YK79